List of References

Abdulla, M.S. and Bhatnagar, S. (2007),

"Parametrized Actor-Critic Algorithms for Finite-Horizon MDPs,"

Proceedings of the American Control Conference, 11-13 July 2007, New York City, USA, pp. 534–539 (paper WeA16.4) (presents two algorithms to compute optimal policies for finite-horizon Markov decision processes).

Top

Abdulla, M.S. and Bhatnagar, S. (2007),

“Reinforcement Learning Based Algorithms for Average Cost Markov Decision Processes,”

Discrete Event Dynamic Systems, vol. 17, pp. 23–52 (application of SPSA in actor-critic algorithms for solution of infinite horizon Markov decision processes).

Top

Abdulsadda, A.T. and Iqbal, K. (2011),

"An Improved SPSA Algorithm for System Identification Using Fuzzy Rules for Training Neural Networks,"

International Journal of Automation and Computing, vol. 8(3), pp. 333–339 (version of algorithm that entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability).

Top

Aboulaich, R., Ellaia R., and El Moumen S. (2010),

"The Mean-Variance-CVaR model for Portfolio Optimization Modeling using a Multi-Objective Approach Based on a Hybrid Method,”

Mathematical Modelling of Natural Phenomena, vol. 5(7), pp. 103−108 (considers problem of portfolio selection, by using the normal boundary intersection approach based on a new hybrid algorithm of SPSA and simulated annealing).

Top

Aksakalli, V. and Ursu, D. (2006),

"Control of Nonlinear Stochastic Systems: Model-Free Controllers versus Linear Quadratic Regulators,"

Proceedings of the IEEE Conference on Decision and Control, 13–15 December 2006, San Diego, CA, USA, pp. 4145–4150 (paper ThIP8.3) (comparison of SPSA-based model-free controller to linear quadratic regulators).

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Alessandri, A., Bolla, R., Grassia, A.F., and Reppetto, M. (2006),

"Identification of Freeway Macroscopic Models using Information from Mobile Phones,"

Proceedings of the American Control Conference, 14–16 June 2006, Minneapolis, MN, pp. 3801–3806 (paper ThC08.5) (parameter estimation for a freeway traffic flow model).

Top

Alessandri, A. and Parisini, T. (1997),

"Nonlinear Modelling of Complex Large-Scale Plants Using Neural Networks and Stochastic Approximation,"
IEEE Transactions on Systems, Man, and Cybernetics — A, vol. 27, pp. 750-757 (model parameter estimation/fault detection).
Top

Altaf, M.U., Heemink, A.W., Verlaan M., and Hoteit, I. (2011),

"Simultaneous Perturbation Stochastic Approximation for Tidal Models,"

Ocean Dynamics, vol. 61(8), pp. 1093−1105 (automated calibration for tidal calibration of

Dutch continental shelf model, which is used to forecast storm surges in the North Sea).

Top

Antal, C., Granichin, O., and Levi, S. (2010),

"Adaptive Autonomous Soaring of Multiple UAVs using Simultaneous Perturbation Stochastic Approximation,"

Proceedings of the 49th IEEE Conference on Decision and Control, Atlanta, GA, 15−17 Dec. 2010, pp. 3656−3661 (algorithm for maximizing flight duration of a single UAV [Uninhabited Air Vehicle] and group of UAVs using thermal model developed at NASA−Dryden; SPSA used for detection of center of thermal updraft where vertical velocity of air stream is highest).

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Arahal, M.R., Cirre, C.M., and Berenguel, M. (2008),

"Serial Grey-Box Model of a Stratified Thermal Tank for Hierarchical Control of a Solar Plant,"

Solar Energy, vol. 82(5), pp. 441–451 (building model for control for existing small solar power system in Spain).
Top

Balakrishna, R., Antoniou, C., Ben-Akiva, M., Koutsopoulos, H.N., and Wen, Y. (2007),

"Calibration of Microscopic Traffic Simulation Models: Methods and Application,"

Transportation Research Record: Journal of the Transportation Research Board, no. 1999, pp. 198-207 (large-scale application to building traffic simulation, with emphasis on practical issues).

Top

Balakrishna, R. and Koutsopoulos, H.N. (2008),

"Incorporating Within-Day Transitions in the Simultaneous Off-line Estimation of Dynamic Origin-Destination Flows without Assignment Matrices,"

Transportation Research Record: Journal of the Transportation Research Board, no. 2085, pp. 31–38 (off-line method for estimating dynamic origin−destination matrices without using assignment matrices).

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Baltcheva, I., Cristea, S., Vázquez-Abad, F.J., and De Prada, C. (2003),

"Simultaneous Perturbation Stochastic Approximation for Real-time Optimization of Model Predictive Control,"
Proceedings of the First Industrial Simulation Conference (ISC 2003), Valencia, Spain, June 2003, pp. 533-537 (global optimization for model predictive control).
Top

Bangerth, W., Klie, H., Matossian, V., Parashar, M., Wheeler, M.F. (2006),
	"An Autonomic Reservoir Framework for the Stochastic Optimization of Well Placement," Cluster Computing, vol. 8(4), pp. 255–269 (use of SPSA with simulator for optimal placement of wells in oil and environmental applications). Top
Bartkute, V. and Sakalauskas, L. (2006),
	"Application of Stochastic Approximation in Technical Design," in Computer Aided Methods in Optimal Design and Operations (I. Bogle and J. Zilinskas, eds.), Series on Computers and Operations Research, vol. 7, World Scientific Publishers, Singapore, pp. 29–38 (development of termination rule based on best function values provided during optimization). Top
Bartkute, V. and Sakalauskas, L. (2007),
	"Simultaneous Perturbation Stochastic Approximation for Nonsmooth Functions," European Journal on Operational Research, vol. 181(3), pp. 1174–1188 (modification of SPSA to have convergence with nondifferentiable functions). Top
Bertsimas, D., Nohadani, O., and Teo, K.M. (2007),
	"Robust Optimization in Electromagnetic Scattering Problems," Journal of Applied Physics, vol. 101(7), article no. 074507 (comparison of gradient-based and gradient-free [SPSA] optimization for electromagnetic scattering problems with many degrees of freedom). Top

Bhatnagar, S. (2005),

"Adaptive Multivariate Three-Timescale Stochastic Approximation Algorithms for Simulation Based Optimization,"
ACM Transactions on Modeling and Computer Simulation, vol. 15, pp. 74–107 (advanced methods for Hessian matrix estimation in the context of simulation optimization; applications in queuing networks).
Top

Bhatnagar, S. (2007),

"Adaptive Newton-Based Multivariate Algorithms for Simulation Optimization,"

ACM Transactions on Modeling and Computer Simulation, vol. 18, pp. 2:1–2:35 (develops several convolution-based [smoothed functional] methods for Hessian matrix estimation in the context of simulation optimization).

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Bhatnagar, S. (2010),

"An Actor–Critic Algorithm with Function Approximation for Discounted Cost Constrained Markov Decision Processes,"

Systems and Control Letters, vol. 59(12), pp. 760–766 (actor–critic reinforcement learning that incorporates a temporal difference critic; Markov decision process when the state and action spaces can be large).

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Bhatnagar, S. (2011),

“Simultaneous Perturbation and Finite Difference Methods,”

in Wiley Encyclopedia of Operations Research and Management Science (J. Cochran, ed.), vol. 7, pp. 4969−4991, Wiley, Hoboken, NJ (general review of SPSA and FDSA, including several extensions).

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Bhatnagar, S. and Abdulla, M.S. (2006),

"A Reinforcement Learning Based Algorithm for Finite Horizon Markov Decision Processes,"

Proceedings of the 45th IEEE Conference on Decision and Control, 13–15 December 2006, San Diego, CA, USA, pp. 5519–5524 (paper FrB09.1) (simulation-based algorithm for finite-horizon Markov decision processes with finite state and finite action space).

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Bhatnagar, S. and Babu, K. (2008),

"New Algorithms of the Q-learning Type,"

Automatica, vol. 44(4), pp. 1111-1119 (two algorithms for Q-learning that use the two-timescale stochastic approximation methodology; work covers areas of reinforcement learning and Markov decision processes).

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Bhatnagar, S. and Borkar, V.S. (2003),

"Multiscale Chaotic SPSA and Smoothed Functional Algorithms For Simulation Optimization,"
Simulation, vol. 79, pp. 568-580 (use of chaos-based generator for random perturbation sequences in high dimensions).
Top

Bhatnagar, S. and Kowshik, H.J. (2005),

"A Discrete Parameter Stochastic Approximation Algorithm for Simulation Optimization,"

Simulation, vol. 81(11), pp. 757–772 (discrete parameter optimization with application to admission control in communication networks).

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Bhatnagar, S. and Kumar, S. (2004),

“A Simultaneous Perturbation Stochastic Approximation-Based Actor—Critic Algorithm for Markov Decision Processes,”
IEEE Transactions on Automatic Control, vol. 49, pp. 592–598 (alternative to dynamic programming for Markov decision processes).
Top

Bhatnagar, S. and Reddy, I. (2005),

"Optimal Threshold Policies for Admission Control in Communication Networks via Discrete Parameter Stochastic Approximation,"
Telecommunications Systems, vol. 29, pp. 9–31 (admission control of packets in communication networks).
Top

Bhatnagar, S., Fu, M.C., and Marcus, S.I. (1999),

"Rate-Based ABR Flow Control Using Two Timescale SPSA,"
Proceedings of the SPIE — The International Society for Optical Engineering, vol. 3841, pp.142-149 (simulation-based optimization for networks).
Top

Bhatnagar, S., Fu, M.C., and Marcus, S.I. and Bhatnagar, S. (2001),

"Two Timescale Algorithms for Simulation Optimization of Hidden Markov Models,''
IIE Transactions, vol. 33, pp. 245-258 (simulation-based optimization using two timescale SPSA).
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Bhatnagar, S., Fu, M.C., and Marcus, S.I., and Fard, P.J. (2001),

"Optimal Structured Feedback Policies for ABR Flow Control Using Two Timescale SPSA,''
IEEE/ACM Transactions on Networking, vol. 9, pp. 479-491 (closed-loop feedback control in ATM networks).
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Bhatnagar, S., Fu, M.C., Marcus, S.I., and Wang, I.J. (2003),

"Two-Timescale Simultaneous Perturbation Stochastic Approximation Using Deterministic Perturbation Sequences,"
ACM Transactions on Modeling and Computer Simulation, vol. 13, pp. 180-209 (use of deterministic—vs. random—perturbation vectors).
Top

Boon, J. (2007),

"Generating Exact D-Optimal Designs for Polynomial Models,"

Proceedings of the Spring Simulation Multiconference, 25–29 March 2007, Norfolk, VA, USA, pp. 121–126 (comparison of SPSA, random search, and standard exchange algorithms for finding D-optimal experimental designs).

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Brooks, O. (2007),

"Solving Discrete Resource Allocation Problems using the Simultaneous Perturbation Stochastic Approximation (SPSA) Algorithm,"

Proceedings of the Spring Simulation Multiconference, 25–29 March 2007, Norfolk, VA, USA, pp. 55–62 (comparing the performance of stochastic optimization algorithms when applied to discrete resource allocation problems).

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Burnett, R. (2004),

“Application of Stochastic Optimization to Collision Avoidance,”
Proceedings of the American Control Conference, 29 June–2 July 2004, Boston, MA, pp. 2789–2794 (comparison of random search, SPSA, and simulated annealing in marine vessel traffic management).
Top

Cao, X. (2011),

“Preliminary Results on Non-Bernoulli Distribution of Perturbations for Simultaneous Perturbation Stochastic Approximation,”

Proceedings of the American Control Conference, 29 June-1 July 2011, San Francisco, CA, pp. 2669−2670 (paper ThB10.6) (considers use of one type of non-Bernoulli distribution for the components of perturbation vector; distribution considered is “segmented uniform,” which meets theoretical conditions for perturbations).

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Cauwenberghs, G. (1994),

"Analog VLSI Autonomous Systems for Learning and Optimization,"
Ph.D. dissertation, California Institute of Technology (issues in hardware implementation for neural networks).
Top

Cauwenberghs, G. (1996),

"An Analog VLSI Recurrent Neural Network Learning a Continuous-Time Trajectory,"
IEEE Transactions on Neural Networks, vol. 7, pp. 346-361 (first demonstration of SPSA dynamic optimization on an analog VLSI chip).
Top

Cauwenberghs, G. (1997),

"Analog VLSI Stochastic Perturbative Learning Architectures,"
International Journal of Analog Integrated Circuits and Signal Processing, vol. 13, pp. 195-209 (extension of SPSA to reinforcement learning; VLSI implementation).
Top

Chan, B.L., Doucet, A., and Tadic, V.B. (2003),

"Optimisation of Particle Filters Using Simultaneous Perturbation Stochastic Approximation,"
Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, April 2003, Hong Kong, vol. VI, pp. 681-684 (optimization of particle filter methods, a.k.a. sequential Monte Carlo methods).
Top

Cheema, J.S., Sankpal, N.V., Tambe, S.S., and Kulkarni, B.D. (2002),

"Genetic Programming Assisted Stochastic Optimization Strategies for Optimization of Glucose to Gluconic Acid Fermentation,"
Biotechnolgy Progress, vol. 18, pp. 1356-1365 (optimization of inputs for genetic programming via SPSA).
Top

Chen, H.F., Duncan, T.E., and Pasik-Duncan, B. (1996),

"A Stochastic Approximation Algorithm with Random Differences,"
Proceedings of the 13^th IFAC World Congress, vol. H, 30 June-5 July 1996, San Francisco, CA, pp. 493-496 (alternative convergence conditions for SPSA).
Top

Chien, S.I. and Luo, J. (2008),

"Optimization of Dynamic Ramp Metering Control with Simultaneous Perturbation Stochastic Approximation,"

Control and Intelligent Systems, scheduled for fall 2008 issue (dynamic ramp metering traffic control to maximize the total throughput subject to the constraints of link densities, capacities, and metering rates).

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Chin, D.C. (1994),

"A More Efficient Global Optimization Algorithm Based on Styblinski and Tang,"
Neural Networks, vol. 7, pp. 573-574 (global optimization implementation).
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Chin, D.C. (1996),

"Efficient Identification Procedure for Inversion Processing,"
Proceedings of the IEEE Conference on Decision and Control, 11-13 December 1996, Kobe, Japan, pp. 3129-3130 (nonlinear signal inversion).
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Chin, D.C. (1997),

"Comparative Study of Stochastic Algorithms for System Optimization Based on Gradient Approximations,"
IEEE Transactions on Systems, Man, and Cybernetics — B, vol. 27, pp. 244-249 (theoretical and numerical efficiency analysis).
Top

Chin, D.C. (1999),

"The Simultaneous Perturbation Method for Processing Magnetospheric Images,"
Optical Engineering, vol. 38, pp. 606-611 (a nonlinear regression-type problem with comparisons to simulated annealing).
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Chin, D.C. and Smith, R.H. (1994),

"A Traffic Simulation for Mid-Manhattan with Model-Free Adaptive Signal Control,"
Proceedings of the Summer Computer Simulation Conference, pp. 296-301 (traffic control application).
Top

Chin, D.C. and Srinivasan, R. (1997),

"Electrical Conductivity Object Locator: Location of Small Objects Buried at Shallow Depths,"
Proceedings of the Unexploded Ordnance (UXO) Conference, pp. 50-57 (application with finite-element models).
Top

Chin, D.C., Srinivasan, R., and Ball, R.E. (1999),

"Discrimination of Buried Plastic and Metal Objects in Subsurface Soil,"
in Information Processing for Remote Sensing, ed., C.H. Chen, World Scientific, New Jersey, pp. 565-570 (application in mine detection).
Top

Chin, D. C., Srinivasan, R., and Ball, R.E. (2001),

"3-D Discrimination of Buried Object in Subsurface Soil via Magnetic Sensors,"
Proceedings of the American Control Conference, 25-27 June 2001, Arlington, VA, pp. 1369-1374 (inverse model estimation via two-dimensional measurements).
Top

Chinthalapati, V.L.R. and Bhatnagar, S. (2006),

"A Simultaneous Deterministic Perturbation Actor-Critic Algorithm with an Application to Optimal Mortgage Refinancing,"

Proceedings of the 45th IEEE Conference on Decision and Control, 13-15 December 2006, San Diego, CA, USA, pp. 4151–4156 (paper ThIP8.4) (SPSA with deterministic perturbations for enhanced performance with an application to a problem of mortgage refinancing).

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Christensen, D.J., Sprowitz A., and Ijspeert A.J. (2010),

“Distributed Online Learning of Central Pattern Generators in Modular Robots,”

From Animals to Animats 11 (S. Doncieux et al., eds), Lecture Notes in Artificial Intelligence, vol. 6226, pp. 402−412, Springer Berlin/Heidelberg (online learning of locomotion gaits for modular robots; considers effects of module failures, different robot morphologies, and rough terrains).

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Cipriani, E. Florian, M., Mahut, M., and Nigro, M. (2011),

“A Gradient Approximation Approach for Adjusting Temporal Origin-Destination Matrices,”

Transportation Research, Part C—Emerging Technologies, vol. 19(2), pp. 270-282 (method to solve for dynamic traffic demand matrix; solution produces acceptable computational times for off-line applications and uses input traffic counts and speeds, prior origin-demand matrices, and other aggregate demand data for inputs)

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Cole-Rhodes, A., Johnson, K., and LeMoigne, J. (2002),

"Multiresolution Registration of Remote-Sensing Images Using Stochastic Gradient,"
SPIE Aerosense 2002, Wavelet Applications IX, vol. 4738, April 2002, Orlando, FL, pp. 44-55 (image registration with wavelet-based preprocessing).
Top

Cole-Rhodes, A., Johnson, K., LeMoigne, J., and Zavorin, I. (2003),

"Multiresolution Registration of Remote Sensing Imagery by Optimization of Mutual Information Using a Stochastic Gradient,"
IEEE Transactions on Image Processing, vol. 12, pp. 1495-1511 (image registration based on correlation and mutual information).
Top

Constantini, G. and Uncini, A. (2003),

"Real-Time Room Acoustic Response Simulation by an IIR Adaptive Filter,"
Electronics Letters, vol. 39, pp. 330-332 (signal processing application in music).
Top

Cupertino, F., Mininno, E., Naso, D., and Salvatore, L. (2007),

"A Comparison of SPSA method and Compact Genetic Algorithm for the Optimization of Induction Motor Position Control,"

Proceedings of the European Conference on Power Electronics and Applications, 2–5 September 2007, pp. 1–10 (implementation of self-optimizing embedded control schemes for induction motor drives).

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Cupertino, F., Mininno, E., Naso, D., and Turchiano, B. (2006),

"An Experimental Implementation of SPSA Algorithms for Induction Motor Adaptive Control,"

Proceedings of SMCals/06, 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, 24–26 July 2006, Utah State University, Logan, U.S.A., pp. 66–71 (implementation of several versions of SPSA for embedded control).

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Dabbene, F., Gay, P., and Sacco N. (2008),

"Optimisation of Fresh-Food Supply Chains in Uncertain Environments, Part I: Background and Methodology,"

Biosystems Engineering, vol. 99(3), pp. 348–359 (optimization of food supply chains that manages the trade-off between logistical costs and some indices measuring the quality of the food as perceived by the consumer).

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Das, S., Spall, J.C., and Ghanem, R. (2010),

“Efficient Monte Carlo Computation of Fisher Information Matrix Using Prior Information,”

Computational Statistics and Data Analysis, vol. 54(2), pp. 272–289 (considers simultaneous perturbation gradient approximation in a Monte Carlo method for estimating Fisher information matrix in complex statistical models; extends Spall (2005) method to exploits partial knowledge of form of information matrix).

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De Craene, M.S., Macq, B., Marques, F., Salembier, P., and Warfield, S.K. (2008),

"Unbiased Group-Wise Alignment by Iterative Central Tendency Estimation,"

Mathematical Modelling of Natural Phenomena, vol. 3(6), pp. 2−32 (joint alignment of a large collection of segmented images into the same system of coordinates; expectation-maximization [EM] estimation used for hidden variables).

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Dippon, J. and Renz, J. (1994),

"Weighted Means of Processes in Stochastic Approximation,"
Universitat Stuttgart, Mathematisches Institut A, Preprint 94-5 (evaluation of Polyak-Ruppert iterate averaging for SPSA).
Top .

Dippon, J. and Renz, J. (1997),

"Weighted Means in Stochastic Approximation of Minima,"
SIAM Journal of Control and Optimization, vol. 35, pp. 1811-1827 (iterate averaging and optimal rates Top

Dong, N. and Chen, Z. (2012),

“A Novel Data Based Control Method Based Upon Neural Network and Simultaneous Perturbation Stochastic Approximation,”

Nonlinear Dynamics, vol. 67(2), pp. 957–963 (modification of model-free control method to have loss that minimizes both the output error and its rate of change, with the aim of yielding a smoother system output).

of convergence).
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El Moumen, S., Ellaia, R., and Aboulaich, R. (2011),

“A New Hybrid Method for Solving Global Optimization Problem,”

Applied Mathematics and Computation, vol. 218(7), pp. 3265−3276 (presents hybrid method that finds a local minimum using descent method based on SPSA, followed by simulated annealing to escape from current local minimum; process is repeated until convergence).

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Fedin, D.S., Granichin, O.N., Dedkov, Yu.S., and Molodtsov, S.L. (2008),

"Method of Measurements with Random Perturbation: Application in Photoemission Experiments,"

Review of Scientific Instruments, vol. 79(3), paper no. 036103 (application to filtering systematic noise with nonzero mean value in photoemission data; used a series of 50 single-scan photoemission spectra).

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Finck, S. and Beyer, H.-G. (2012),

“Performance Analysis of the Simultaneous Perturbation Stochastic Approximation Algorithm on the Noisy Sphere Model,”

Theoretical Computer Science, vol. 419, pp. 50–72 (theoretical comparison of different algorithms with spherical loss function; method allows for convergence results for non-noisy and noisy optimization to be obtained simultaneously; includes comparison of SPSA and evolution strategies).

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Flaxman, A., Kalai, A.T., and McMahan, H.B. (2005),

“Online Convex Optimization in the Bandit Setting: Gradient Descent Without a Gradient,”
Proceedings of the 16th Symposium on Discrete Algorithms (SODA), Vancouver, Canada, pp. 385–394 (distribution-free analysis with very few assumptions).
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Fu, M.C. and Hill, S.D. (1997),

"Optimization of Discrete Event Systems via Simultaneous Perturbation Stochastic Approximation,"
Transactions of the Institute of Industrial Engineers, vol. 29, pp. 233-243 (simulation-based optimization for discrete-event and queueing networks).
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Garrett, J. (2004),

"Jointly Optimizing Model Complexity and Data-Processing Parameters with Mixed-Input SPSA,"
36th Symposium on the Interface: Computing Science and Statistics, 26–29 May 2004, Baltimore, MD (optimization in predictive modeling and classification of feature-selection and model-complexity parameters).
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Gerencsér, L. (1997),

"Rate of Convergence of Moments of Spall's SPSA Method,"
in Stochastic Differential and Difference Equations (Csiszár and Gy. Michaletzky, eds.), Series on Progress in Systems and Control Theory, vol. 23, Birkhäuser, Boston, pp. 67-75 (convergence conditions for means and other moments of SPSA iterate).
Top

Gerencsér, L. (1999),

"Convergence Rate of Moments in Stochastic Approximation with Simultaneous Perturbation Gradient Approximation and Resetting,"
IEEE Transactions on Automatic Control, vol. 44, pp. 894-905 (convergence conditions for moments of SPSA iterate).
Top

Gerencsér, L. and Vágó, Z. (1999),

"Stochastic Approximation for Function Minimization Under Quantization Error,"
Proceedings of the IEEE Conference on Decision and Control, 7-10 December 1999, Phoenix, AZ, pp. 2373-2376 (convergence analysis for optimization problems with non-random noise).
Top

Gerencsér, L. and Vágó, Z. (2000),

"SPSA in Noise-Free Optimization,"
Proceedings of the American Control Conference, 28-30 June 2000, Chicago, IL, pp. 3284-3288 (theoretical analysis of convergence rate when loss measurements are noise-free).
Top

Gerencsér, L. and Vágó, Z. (2001),

"The Mathematics of Noise-Free SPSA,"
Proceedings of the IEEE Conference on Decision and Control, 4-7 December 2001, Orlando, FL, pp. 4400-4405 (analysis of convergence rate with noise-free loss measurements).
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Gerencsér, L., Hill, S.D., Vágó, Z. (1999),

"Optimization over Discrete Sets via SPSA,"
Proceedings of the IEEE Conference on Decision and Control, 7-10 December 1999, Phoenix, AZ, pp. 1791-1795 (use of SPSA for discrete optimization).
Top

Gerencsér, L., Hill, S.D., and Vágó, Z. (2001),

"Discrete Optimization via SPSA,"
Proceedings of the American Control Conference, 25-27 June 2001, Arlington, VA, pp. 1503-1504 (SPSA for discrete optimization, including new rate of convergence result).
Top

Gerencsér, L., Hill, S.D., and Vágó, Z. (2002),

"Discrete Optimization, SPSA, and Markov Chain Monte Carlo,"
Proceedings of the IEEE Conference on Decision and Control, December 2002, Las Vegas, NV, pp. 2346-2347 (use of MCMC methods with SPSA).
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Gerencsér, L., Hill, S.D., Vágó, Z., and Vincze, Z. (2004),

"Discrete Optimization, SPSA, and Markov Chain Monte Carlo Methods,"
Proceedings of the American Control Conference, 29 June–2 July 2004, Boston, MA, pp. 3814–3819 (minimization of discrete convex functions and connection to MCMC).
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Gerencsér, L., Kozmann, G., and Vágó, Z. (1998),

"SPSA for Non-Smooth Optimization with Application in ECG Analysis,"
Proceedings of the IEEE Conference on Decision and Control, 16-18 December 1998, Tampa, FL., pp. 3907-3908 (application in a classification problem).
Top

Gerencsér, L., Kozmann, G., Vágó, Z., and Haraszti, K. (2002),

"The Use of the SPSA Method in ECG Analysis,”
IEEE Transactions on Biomedical Engineering, vol. 49, pp. 1094-1101 (application in biomedical classification problem).
Top

Gosavi, A. (2002),

"The Effect of Noise on Artificial Intelligence and Meta-Heuristic Techniques,”

Proceedings of the Artificial Neural Networks in Engineering Conference (Intelligent Engineering Systems Through Artificial Neural Networks), American Society of Mechanical Engineering Press, vol. 12, pp. 981-988 (effect of simulation-based noise on simulated annealing and SPSA).
Top

Gosavi, A. (2003),

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning,
Kluwer Academic, Boston (book including significant discussion of SPSA in simulation-based optimization, especially in Sects. 7.2.1 and 12.4.3).
Top

Gosavi, A., Ozkaya, E., and Kahraman, A. (2007),

"Simulation Optimization for Revenue Management of Airlines with Cancellations and Overbooking,"

OR Spectrum (special issue on revenue management), vol. 29, pp. 21-38 (simulation-based solution to the seat allocation problem in the airline industry).

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Graf, M. and Kimms, A. (2011),

“An Option-Based Revenue Management Procedure for Strategic Airline Alliances,”

European Journal of Operational Research, vol. 215(2), pp. 459−469 (simulation-based optimization of airline capacity control process: whether to accept an incoming customer request for a seat or reject it in hope that another customer will request the seat later at a higher price).

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Granichin, O.N. (2002),

"Randomized Algorithms for Stochastic Approximation Under Arbitrary Disturbances,"
Automation and Remote Control, vol. 63, pp. 209-219 (handles general noise distributions, with connections to quantum computing).
Top

Granichin, O.N. (2003),

"Optimal Convergence Rate of the Randomized Algorithms of Stochastic Approximation in Arbitrary Noise,"
Automation and Remote Control, vol. 64, pp. 252-262 (optimization of algorithm parameters with dependence on distribution of the simultaneous perturbation and smoothness of loss function).
Top

Granichin, O., Gurevich, L., and Vakhitov, A. (2009),

“SPSA with a Fixed Gain for Intelligent Control in Tracking Applications,”

Proceedings of the IEEE International Conference on Control Applications, 8−10 July 2009, St. Petersburg, Russia, pp. 1415−1420 (fixed gain SPSA is considered for problem of tracking a changing minimum point; paper establishes an upper bound to mean square estimation error in case of once differentiable loss and almost arbitrary noises).

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Granichin, O.N. and Izmakova, O.A. (2005),

"A Randomized Stochastic Approximation Algorithm for Self-Learning,"

Automation and Remote Control, vol. 66(8), pp. 1239-1248 (SPSA-based self-learning [unsupervised] algorithms).

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Granichin O.N. and Polyak, B.T. (2003),

Randomized Algorithms of Estimation and Optimization Under Almost Arbitrary Noises,
Nauka, Moscow, Russia (English language contents for a book in Russian; thorough treatment of SPSA and related algorithms under general noise conditions).

Granichin, O.N. and Vakhitov, A.T. (2006),

"Accuracy for the SPSA Algorithm with Two Measurements,"

WSEAS Transactions on Systems, vol. 5(5), pp. 953-957 (proves that moments of degree from 1 to 2 for the estimates are convergent).

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Grigaitis D., Bartkute V., and Sakalauskas L. (2007),

"An Optimization of System for Automatic Recognition of Ischemic Stroke Areas in Computed Tomography Images,"

Informatica, vol. 18(4), pp. 603-614 (application to automatic recognition of ischemic stroke area on computed tomography [CT] images).

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Grote, D.P., Henestroza, E., and Kwan, J.W. (2003),

"Design and Simulation of a Multibeamlet Injector for a High Current Accelerator,"
Physical Review Special Topics—Accelerators and Beams, vol. 6, pp. 14202-1-14202-12 (optimal design for parts of a particle accelerator).
Top

Gu, W.F., Xiang, C., Venkatesh, Y.V., Huang, D., and Lin, H. (2012),

"Facial Expression Recognition using Radial Encoding of Local Gabor Features and Classifier Synthesis,"

Pattern Recognition, vol. 45(1), pp. 80−91 (SPSA used for estimating parameters in model new facial expression recognition scheme that is motivated by some characteristics of the human visual cortex).

Top

Guo, C., Song, Q., and Cai, W. (2007),

"A Neural Network Assisted Cascade Control System for Air Handling Unit,"

IEEE Transactions on Industrial Electronics, vol. 54(1), pp. 620-628 (implementation in control for a heating, ventilating and air-conditioning system, and comparison with traditional proportional-integral-derivative [PID] controllers).

Top

Güven, T., La, R.J., Shayman, M.A., and Bhattacharjee, B. (2006),

"Measurement-Based Optimal Routing on Overlay Architectures for Unicast Sessions,"

Computer Networks, vol. 50(12), pp. 1938–1951 (routing algorithm for load-balance of intra-domain traffic along multiple paths for multiple unicast sources; comparison with existing MATE routing algorithm).

Top

Hahn, B. and Oldham, K. R. (2010),

"A Model-Free On-Off Iterative Adaptive Controller Based on Stochastic Approximation,"

Proceedings of the American Control Conference, 30 June−2 July 2010, Baltimore, MD, USA, pp. 1665−1670 (paper WeC03.2) (adaptive controller applicable to servo systems performing repeated motions under strict power constraints).

Top

Hao, L. and Yao, M. (2011),

"SPSA-based step tracking algorithm for mobile DBS reception,"

Simulation Modelling Practice and Theory, vol. 19(2), pp. 837–846 (step tracking algorithm for Ku-band mobile satellite communication used in mobile direct broadcasting satellite [DBS] reception).

Top

He, Y., Fu, M.C, and Marcus, S.I. (2003),

"Convergence of Simultaneous Perturbation Stochastic Approximation for Nondifferentiable Optimization,"
IEEE Transactions on Automatic Control, vol. 48, pp. 1459-1463 (considers convergence for continuous but non-differentiable functions).
Top

Heydon, B.D. and Hill, S.D. (2003),

"Maximizing Target Damage Through Optimal Aimpoint Patterning,"

AIAA 3rd Biennial National Forum on Weapon System Effectiveness, 18-20 November 2003, Seal Beach, CA (simulation-based optimization to aid in targeting and minimizing collateral damage) (distribution restricted to U.S. government agencies and their contractors).

Top

Hill, S.D. (2005),

“Discrete Stochastic Approximation with Application to Resource Allocation,”
Johns Hopkins APL Technical Digest, vol. 26, pp. 15–21 (application to discrete optimization with discussion of resource allocation problem).
Top

Hill, S.D. and Fu, M.C. (1995),

"Transfer Optimization via Simultaneous Perturbation Stochastic Approximation,"
Proceedings of the Winter Simulation Conference, eds. C. Alexopoulos, K. Kang, W.R. Lilegdon, and D. Goldsman, pp. 242-249 (transit systems and queuing networks).
Top

Hill, S.D., Gerencsér, L., and Vágó, Z. (2003),

"Stochastic Approximation on Discrete Sets Using Simultaneous Perturbation Difference Approximations,"
Proceedings of the 37th Conference on Information Science and Systems, 12-14 March 2003, The Johns Hopkins University, CD-ROM Paper #17 (discrete optimization method).
Top

Hill, S.D., Gerencsér, L., and Vágó, Z. (2004),

“Stochastic Approximation on Discrete Sets Using Simultaneous Difference Approximations,”
Proceedings of the American Control Conference, 29 June–2 July 2004, Boston, MA, pp. 2795–2798 (discrete minimization, including some convergence theory).
Top

Hirokami, T., Maeda, Y., and Tsukada, H. (2006),

"Estimation using Simultaneous Perturbation Stochastic Approximation,"

Electrical Engineering in Japan, vol. 154(2), pp. 30–39 (parameter estimation, including a convergence theorem and simulation study).

Top

Hong, Y.-Y., Chang, H.-L., and Chiu, C.-S. (2010),

"Hour-Ahead Wind Power and Speed Forecasting using Simultaneous Perturbation Stochastic Approximation (SPSA) Algorithm and Neural Network with Fuzzy Inputs,"

Energy, vol. 35(9), pp. 3870−3876 (proposes method of wind power and speed forecasting using a multi-layer feed-forward neural network with SPSA-based training; real wind power generation and wind speed data measured at a wind farm are used for simulation).

Top

Hopkins, H.S. (1997),

"Experimental Measurement of a 4-D Phase Space Map of a Heavy Ion Beam,"
Ph.D. thesis, Dept. of Nuclear Engineering, University of California-Berkeley, December 1997 (control to reduce alignment errors in targeting of ion beam).
Top

Hu, J., Zhu, W., Su, Y., and Wong, W. K. (2010),

"Controlled Optimal Design Program for the Logit Dose Response Model,"

Journal of Statistical Software, vol. 35(6), pp. 1−17 (generating controlled D-optimal and other designs for dose-response studies, which can incorporate prior information and multiple objectives; combined with cross-entropy method for optimization).

Top

Hutchison, D.W. (2002),

"On an Efficient Distribution of Perturbations for Simulation Optimization Using Simultaneous Perturbation Stochastic Approximation,"
Proceedings of the IASTED International Conference on Applied Modelling and Simulation, 4-6 November 2002, Cambridge, MA, pp. 440-445 (careful empirical evaluation of Bernoulli and non-Bernoulli perturbation distributions).
Top

Hutchison, D.W. and Hill, S.D. (2000),

"Simulation Optimization of Airline Delay Using Simultaneous Perturbation Stochastic Optimization,"
Proceedings of the 33rd Annual Simulation Symposium, 16-20 April 2000, Washington, DC, pp. 253-258 (application in simulation-based optimization).
Top

Hutchison, D.W. and Hill, S.D. (2001),

"Simulation Optimization of Airline Delay with Constraints,"
Proceedings of the 2001 Winter Simulation Conference, 9-12 December 2001, Arlington, VA, pp. 1017-1022 (resource allocation in airline networks).
Top

Ji, X.D. and Familoni, B.D. (1996),

"Experimental Study of Direct Adaptive SPSA Control System with Diagonal Recurrent Neural Network Controller,"
Proceedings of the IEEE SoutheastCon '96, pp. 525-528 (evaluation with recurrent neural networks).
Top

Ji, X.D. and Familoni, B.D. (1999),

"A Diagonal Recurrent Neural Network-Based Hybrid Direct Adaptive SPSA Control System,"
IEEE Transactions on Automatic Control, vol. 44, pp. 1469-1473 (a hybrid SPSA/PID adaptive controller with a recurrent neural network as function approximator).
Top

Johannsen, D.A., Wegman, E.J., Solka, J.L., and Priebe, C.E. (2004),

"Simultaneous Selection of Features and Metric for Optimal Nearest Neighbor Classification,"
Communications in Statistics–Theory and Methods, vol. 33, pp. 2137-2157 (pattern recognition application).
Top

Kiranyaz, S., Ince, T., and Gabbouj, M. (2011),

"Stochastic Approximation Driven Particle Swarm Optimization with Simultaneous Perturbation—Who will Guide the Guide?,"

Applied Soft Computing, vol. 11(2), pp. 2334−2347 (SPSA used to guide particle swarm optimization [PSO], with finding that even if SPSA parameters are not tuned well, results of SA-driven PSO are still better than the best of PSO alone).

Top

Kizito, R., Roggemann, M.C., Schulz, T.J., and Zhang, Y. (2004),

"Image Sharpness Metric-Based Deformable Mirror Control for Beam Projection Systems Operating in Strong Scintillation,"
Proceedings of the SPIE—The International Society for Optical Engineering, vol. 5160, pp. 406–416 (controlling a deformable mirror in beam projection systems).
Top

Kleinman, N.L. (1996),

"Stochastic Approximation Algorithms: Theory and Applications,"
Ph.D. thesis, Dept. of Mathematical Sciences (now Dept. of Applied Mathematics and Statistics), The Johns Hopkins University (use of SPSA in simulation-based optimization and Berry-Esseen-type rates of convergence to normality).
Top

Kleinman, N.L., Hill, S.D., and Ilenda, V.A. (1997),

"SPSA/SIMMOD Optimization of Air Traffic Delay Cost,"
Proceedings of the American Control Conference, 4-6 June 1997, Albuquerque, NM, pp. 1121-1125 (applications to air traffic network).
Top

Kleinman, N.L., Spall, J.C., and Naiman, D.Q. (1999),

"Simulation-Based Optimization with Stochastic Approximation Using Common Random Numbers,"
Management Science, vol. 45, pp. 1570-1578 (evaluation of SPSA and finite-difference methods in simulation-based optimization when common random numbers are feasible).
Top

Klie, H., Bangerth, W., Wheeler, M., Parashar, M., and Matossian, V. (2004),

“Parallel Well Location Optimization Using Stochastic Algorithms on the Grid Computational Framework,”
Proceedings of the 9th European Conference on the Mathematics of Oil Recovery (ECMOR IX), CD-ROM (optimizing oil well locations using SPSA).
Top

Koch, M.I., Chin, D.C., and Smith, R.H. (1997),

"Network-Wide Approach to Optimal Signal Light Timing for Integrated Transit Vehicle and Traffic Operations,"
Proceedings of the 7^th National Conference on Light Rail Transit, vol. 2, National Academy of Sciences Press, pp. 126-131 (control of traffic and transit vehicles at a network-wide level).
Top

Kocsis, L., Szepesvári, Cs. and Winands, M.H.M. (2005),

"RSPSA: Enhanced Parameter Optimisation in Games,"
Proceedings of the 11th Advances in Computer Games Conference (ACG-11), 6–8 September 2005, Taipei, Taiwan (optimizing parameters of game programs: poker, lines of actions; uses common random numbers and antithetic random numbers).
Top

Kocsis, L. and Szepesvári, C. (2006),

"Universal Parameter Optimisation in Games Based on SPSA,"

Machine Learning, vol. 63(3), pp. 249–286 (tuning of the large number of parameters that are crucial for the performance of automated game-playing algorithms).

Top

Kong, X., Yang, Y., Chen, X., Shao, Z., and Gao, F. (2011),

“Quality Control via Model-Free Optimization for a Type of Batch Process with a Short Cycle Time and Low Operational Cost,”

Industrial and Engineering Chemistry Research, vol. 50(5), pp. 2994–3003 (model-free optimization method for a batch process with short cycle time and low operational cost is proposed to improve the efficiency of quality control; demonstration on quality control of injection molding process)

Top

Kothandaraman, G. and Rotea, M.A. (2003),

"SPSA Algorithm for Parachute Parameter Estimation,"
AIAA Paper No. 2003–2118, 17th AIAA Aerodynamic Decelerator Systems Technology Conference and Seminar, Monterey, CA, May 2003, pp. 138–148 (parameter estimation for six-degree-of-freedom parachute model).
Top

Kothandaraman, G. and Rotea, M.A. (2005),

"SPSA Algorithm for Parachute Parameter Estimation,"

Journal of Aircraft, vol. 42(5), pp. 1229–1235 (algorithm to estimate unknown parameters of parachute models from flight-test data without use of analytical gradients; algorithm is used to estimate aerodynamic and apparent mass coefficients in model).

Top

Lambert, P. and Banchs, R.E. (2007),

"SPSA vs Simplex in Statistical Machine Translation Optimization,"

PAMM − Proc. Appl. Math. Mech., vol. 7(1), pp. 1062503–1062504 (comparison using IWSLT 2005 Chinese-English data; both methods showed similar performance, but SPSA was more robust to the choice of initial settings).

Top

Lambert, P., Banchs, R.E., and Crego, J.M. (2007),

"Discriminative Alignment Training without Annotated Data for Machine Translation,"

Proceedings Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume, Short Papers, April 2007, Rochester, NY, USA, pp. 85–88 (machine learning application in the area of translations).

Top

Lee, J.-B. and Ozbay, K. (2008),

"Calibration of a Macroscopic Traffic Simulation Model Using Enhanced Simultaneous Perturbation Stochastic Approximation Methodology,"

Transportation Research Board Annual Meeting paper no. 08-2964 (parameter estimation for traffic simulation based on a Bayesian sampling approach; includes example calibration for a portion of I-880 in California).

Top

Luman, R.R. (1997),

"Quantitative Decision Support for Upgrading Complex Systems of Systems,"
Ph.D. thesis, School of Engineering and Applied Science, George Washington University (use of first- and second-order SPSA in simulation-based optimization).
Top

Luman, R.R. (2000),

"Upgrading Complex Systems of Systems: A CAIV Methodology for Warfare Area Requirements Allocation,"
Military Operations Research, vol. 5(2), pp. 53-75 (use of first- and second-order SPSA in simulation-based optimization).
Top

Ma, J., Nie, Y., and Zhang, H.M. (2007),

"Solving the Integrated Corridor Control Problem Using Simultaneous Perturbation Stochastic Approximation,"

Transportation Research Board Annual Meeting, Paper #07-1065 (control of traffic in a transportation corridor using traffic signal timing and ramp metering).

Top

Ma, J., Dong, H., and Zhang, H.M. (2007),

"Calibration of Micro Simulation with Heuristic Optimization Methods,"

Transportation Research Board Annual Meeting, Paper #07-1282 (estimation of microscopic traffic simulation; includes comparisons with genetic algorithm and trial-and-error iterative adjustment).

Top

Ma, J., Dong, H., and Zhang, H.M. (2007),

"Calibration of Micro Simulation with Heuristic Optimization Methods,"

Transportation Research Record: Journal of the Transportation Research Board, vol. 1999, pp. 208-217 (slightly updated version of TRB meeting paper above: estimation of microscopic traffic simulation; includes comparisons with genetic algorithm and trial-and-error iterative adjustment).

Top

Madan, D.B. (2010),

"Variance Swap Portfolio Theory,"

Contemporary Quantitative Finance (C. Chiarella and A. Novikov, eds.), Springer Berlin Heidelberg, pp. 183-194 (optimal portfolios of variance swaps are constructed taking account of both autocorrelation and cross asset dependencies).

http://dx.doi.org/10.1007/978-3-642-03479-4_10

Top

Maeda, Y. (1996),

"Time Difference Simultaneous Perturbation Method,"
Electronics Letters, vol. 32, pp. 1016-1018 (method for coping with nonstationarities in dynamic estimation).
Top

Maeda, Y. (2002),

"Real-Time Control and Learning Using Neuro-Controller via Simultaneous Perturbation for Flexible Arm System,"
Proceedings of the American Control Conference, 8-10 May 2002, Anchorage, AK, pp. 2583-2588 (applications for robot arm control).
Top

Maeda, Y. and Kanata, Y. (1994),

"Extended Adaptive Robbins-Monro Procedure Using Simultaneous Perturbation for a Least-Square Approximation Problem,"
Proceedings of the Asian Control Conference, pp. 383-386 (recursive estimation of input-output relationship by least squares).
Top

Maeda, Y. and Maruyama, T. (2003),

"Natural Gradient Using Simultaneous Perturbation Without Probability Densities for Blind Source Separation,"
Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation, Nara, Japan, April 2003, pp. 439-443 (natural gradient method for blind source separation in signal processing).
Top

Maeda, Y. and Toshiki, T. (2003),

"FPGA Implementation of a Pulse Density Neural Network With Learning Ability Using Simultaneous Perturbation,"
IEEE Transactions on Neural Networks, vol. 14, pp. 688-695 (hardware implementation of a pulse neural network).
Top

Maeda, Y. and Tsushio, K. (2002),

"Blind Signal Separation via Simultaneous Perturbation Method,"
Proceedings of the International Joint Conference on Neural Networks, Honolulu, HI, 12-17 May 2002 (method for extracting signals of interest from mixtures of signals).
Top

Maeda, Y. and Wakamura, M. (2005),

"Simultaneous Perturbation Learning Rule for Recurrent Neural Networks and Its FPGA Implementation,"

IEEE Transactions on Neural Networks, vol. 16(6), pp. 1664–1672 (hardware implementation in field-programmable gate array [FPGA]).

Top

Maeda Y. and Yoshida T. (1999),

"An Active Noise Control Without Estimation of Secondary-Path-ANC Using Simultaneous Perturbation,"
Proceedings of the International Symposium on Active Control of Sound and Vibration (ACTIVE '99), 2-4 December 1999, Ft. Lauderdale, FL, pp. 985-994 (active noise control scheme).
Top

Maeda, Y., Hirano, H., and Kanata, Y. (1995),

"A Learning Rule of Neural Networks via Simultaneous Perturbation and its Hardware Implementation,"
Neural Networks, vol. 8, pp. 251-259 (pattern recognition).
Top

Maeda, Y. and De Figueiredo, R.J.P. (1997),

"Learning Rules for Neuro-Controller via Simultaneous Perturbation,"
IEEE Transactions on Neural Networks, vol. 8, pp. 1119-1130 (use in neural network-based control with applications in robotics).
Top

Maeda, Y., Nakazawa, A., and Yakichi, K. (1999),

"Hardware Implementation of a Pulse Density Neural Network Using Simultaneous Perturbation Learning Rule,"
Analog Intergrated Circuits and Signal Processing, vol. 18, pp. 153-162 (neural network training via circuit design with gate operations).
Top

Martin, S., Morison, G., Nailon, W., and Durrani, T. (2004),

“Fast and Accurate Image Registration Using Tsallis Entropy and Simultaneous Perturbation Stochastic Approximation,”
Electronics Letters, vol. 40, pp. 595–597 (Tsallis measure of mutual information is combined with SPSA to register images).
Top

Martinez, J., Sawut, U., and Nakano, K. (2008),

"Application of Non-linear Observer with Simultaneous Perturbation Stochastic Approximation Method to Single Flexible Link SMC,"

Proceedings of SICE 2008—47th Annual Conference of the Society of Instrument and Control Engineers of Japan, Tokyo, Japan, 20−22 August 2008, pp. 2150−2155 vibration control of a one-link flexible arm system; involves parameter estimation for nonlinear observer).

Top

Maryak, J.L. (1997),

"Some Guidelines for Using Iterate Averaging in Stochastic Approximation,"
Proceedings of the IEEE Conference on Decision and Control, 10-12 December 1997, San Diego, CA, pp. 2287-2290 (exploration of iterate averaging for SPSA).
Top

Maryak, J.L., and Chin, D.C. (2001),

"Global Random Optimization by Simultaneous Perturbation Stochastic Approximation,"
Proceedings of the American Control Conference, 25-27 June 2001, Arlington, VA, pp. 756-762 (discusses use of SPSA for global search with and without the use of additional injected noise).
Top

Maryak, J.L., and Chin, D.C. (2008),

"Global Random Optimization by Simultaneous Perturbation Stochastic Approximation,"
IEEE Transactions on Automatic Control, vol. 53, pp. 780-783 (updated version of above ACC 2001 paper on SPSA for global search with and without the use of additional injected noise).
Top

Maryak, J.L. and Spall, J.C. (2005),

"Simultaneous Perturbation Optimization for Efficient Image Restoration,"
IEEE Transactions on Aerospace and Electronic Systems, vol. 41, pp. 356-361 (application in recovering an image from a degraded version of image).
Top

Maryak, J.L., Smith, R.H., and Winslow, R.L. (1998),

"Modeling Cardiac Ion Channel Conductivity: Model Fitting Via Simulation,"
Proceedings of the Winter Simulation Conference (D.J. Medeiros and E.F. Watson, eds.), pp. 1587-1590 (biomedical application involving model fitting; includes comparison with simulated annealing).
Top

McClary, D.W., Syrotiuk, V.R., and Kulahci M (2010),

"Steepest-Ascent Constrained Simultaneous Perturbation for Multiobjective Optimization,"

ACM Transactions on Modeling and Computer Simulation, vol. 21(1), pp. 2:1−2:22 (considers simultaneous optimization of multiple responses in a dynamic system; example in cross-layer optimization of throughput, packet loss, and end-to-end delay in a a self-organizing wireless network).

Top

Merhof, D., Soza, G., Stadlbauer, A., Greiner, G., and Nimsky, C. (2007),

"Correction of Susceptibility Artifacts in Diffusion Tensor Data Using Non-Linear Registration,"

Medical Image Analysis, vol. 11(6), pp. 588-603 (surgical application in image registration to localize major white matter tracts within the human brain; aim to achieve an optimal resection while avoiding post-operative neurological deficits).

Top

Miranda, A.K. and Castillo, E.D. (2011),

"Robust Parameter Design Optimization of Simulation experiments using Stochastic Perturbation Methods,"

Journal of the Operational Research Society, vol. 62, pp. 198–205 (considers SPSA for robust parameter design problems, with two example applications: a single-stage inventory system for which the quality of the solutions was easy to verify, and a more realistic manufacturing system). Top

Mishra, V., Bhatnagar, S., and Hemachandra, N. (2007),

"Discrete Parameter Simulation Optimization Algorithms with Applications to Admission Control with Dependent Service Times,"

Proceedings of the 46th IEEE Conference on Decision and Control, 12-14 December 2007, New Orleans, LA, USA, pp. 2986-2991 (paper ThPI25.4) (comparisons with smoothed functional technique in particular queuing problem with discrete parameter space).

Top

Mohamed, W. and Ben Hamza, A.B. (2010),

"Medical Image Registration Using Stochastic Optimization,"

Optics and Lasers in Engineering, vol.48(12), pp.1213−1223 (maximizes a Tsallis entropy-based divergence using modified SPSA; experimental results demonstrate accuracy of proposed approach in comparison to existing entropic image alignment techniques).

Top

Mostaghimi, M. (1997),

"Modeling Monetary Policy Using SPSA-Based Neural Networks,"
Proceedings of the IEEE Conference on Decision and Control, 10-12 December 1997, San Diego, CA, pp. 492-497 (application in determining macroeconomic policy).
Top

Nandi, S., Ghosh, S., Tambe, S.S., and Kulkarni, B.D. (2001),

"Artificial Neural-Network-Assisted Stochastic Process Optimization Strategies,"
AIChE Journal, vol. 47, pp. 126-141 (process control application; comparisons with genetic algorithms).
Top

Nechyba, M.C. and Xu, Y. (1997),

"Human-Control Strategy: Abstraction, Verification, and Replication,"
IEEE Control Systems Magazine, vol. 17(5), pp. 48-61 (controllers involving human-machine interaction).
Top

Ni, J. and Song, Q. (2006),

"Dynamic Pruning Algorithm for Multilayer Perceptron-Based Neural Control Systems,"

Neurocomputing, vol. 69, pp. 2097–2111 (use of adaptive SPSA in neural network training).

Top

Nicolai, R.P. and Koning, A.J. (2006),

"A General Framework for Statistical Inference on Discrete Event Systems,"

Technical Report EI 2006-45, Econometric Institute, Erasmus University Rotterdam, The Netherlands (common random numbers for parameter estimation in discrete event systems).

Top

Ninan, B.M. (2004),

"Resource Pricing for Connection-Oriented Networks,"

Ph.D. dissertation, North Carolina State University, Operations Research Program, Raleigh, NC (revenue maximization for network pricing).

Top

Ning, Y., Tang, W., and Wang, H. (2005),

"Hybrid Genetic-SPSA Algorithm Based on Random Fuzzy Simulation for Chance-Constrained Programming,"

in Fuzzy Systems and Knowledge Discovery (L. Wang and Y. Jin, eds.), pp. 332–-335, Springer, Berlin (genetic algorithm [GA] is employed to search for the optimal solution in the entire space and SPSA is used to improve the new chromosomes obtained by crossover and mutation at each generation in GA).

Top

Ning, Y., Tang, W., and Guo, C. (2008),

"Simultaneous Perturbation Stochastic Approximation Algorithm Combined with Neural Network and Fuzzy Simulation,"

Transactions of Tianjin University, vol. 14(1), pp. 43–49 (SPSA for solving three kinds of fuzzy programming models: fuzzy expected value model, fuzzy chance-constrained programming model, and fuzzy dependent-chance programming model).

Top

Nusawardhana, N. and Zak, S.H. (2004),

"Simultaneous Perturbation Extremum Seeking Method for Dynamic Optimization Problem,"
Proceedings of the American Control Conference, 29 June–2 July 2004, Boston, MA, pp. 2805–2810 (application to control problems; includes convergence theory).
Top

Ozguven, E.E. and Ozbay, K (2008),

"Simultaneous Perturbation Stochastic Approximation Algorithm for Solving Stochastic Problems of Transportation Network Analysis: Performance Evaluation,"

Transportation Research Record: Journal of the Transportation Research Board, no. 2085, pp. 12–20 (comparison with method of successive averages [MSA] in solving traffic assignment problems with varying levels of stochastic effects).

Top

Palumbo, N.F., Reardon, B.E., and Blauwkamp, R.A. (2004),

"Integrated Guidance and Control for Homing Missiles,"
Johns Hopkins APL Technical Digest, vol. 25, pp. 121–139 (estimation of parameters in a guidance and control model).
Top

Patan, K. and Parisini, T. (2002),

"Stochastic Learning Methods for Dynamic Neural Networks,"
Proceedings of the American Control Conference, 8-10 May 2002, Anchorage, AK, pp. 2577-2582 (comparisons of methods based on simulated and real data).
Top

Popovic, D., Jankovic, M., Magner, S., and Teel A.R. (2006),

"Extremum Seeking Methods for Optimization of Variable Cam Timing Engine Operation,"

IEEE Transactions on Control Systems Technology, vol. 14(3), pp. 398-407 (automotive application where common Bernoulli perturbation distribution is modified to satisfy persistent excitation condition in control).

Top

Poyiadjis, G., Singh, S.S., and Doucet, A. (2006),

"Gradient-free Maximum Likelihood Parameter Estimation with Particle Filters,"

Proceedings of the American Control Conference, 14-16 June 2006, Minneapolis, MN, pp. 3052–3067 (paper ThB08.2) (on-line estimation of parameters in nonlinear/non-Gaussian state-space models with particle filters).

Top

Primavera, A., Palestini, L., Cecchi, S., Piazza, F., and Moschetti, M. (2010),

"A Hybrid Approach for Real-Time Room Acoustic Response Simulation,"

Audio Engineering Society Convention Paper, presented at the 128th Convention, 22−25 May 2010, London, UK (reverberation algorithm for listening to recorded and live music).

Top

Rădac, M.-B., Precup, R.-E., Petriu, E.M., and Preitl, S. (2011),

"Application of IFT and SPSA to Servo System Control,"

IEEE Transactions on Neural Networks, vol. 22(12), pp. 2363−2375 (considers SPSA combined with iterative feedback tuning for estimating parameters of state feedback controllers in linear-quadratic-Gaussian [LQG] problem formulation; implementation case study concerns LQG-based design of angular position controller for direct current servo system laboratory equipment).

Top

Ramanathan, S.P., Mukherjee, S., Dahule, R.K., Ghosh, S., Rahman, I., Tambe, S.S., Ravetkar, D.D., and Kulkarni, B.D. (2001),

"Optimization of Continuous Distillation Columns Using Stochastic Optimization Approaches,"
Transactions of the Institution of Chemical Engineers, vol. 79, pp. 310-322 (evaluation of SPSA and genetic algorithms in a process control problem).
Top

Reardon, B.E., Lloyd, J.M., and Perel, R.Y. (2010),

"Tuning Missile Guidance and Control Algorithms Using Simultaneous Perturbation Stochastic Approximation,"

Johns Hopkins APL Technical Digest, vol. 29(1), pp. 85–100 (automated tuning process for adjustable parameters in guidance and control algorithms for missiles).

Top

Reardon, B.E., Palumbo, N.F., and Casper, S.G. (2002),

"Simulation-Based Performance Optimization of Missile Guidance and Control Algorithms,"
11th Annual AIAA/MDA Technology Conference and Exhibit, 29 July-2 August 2002, Williamsburg, VA (design of algorithms under constraints; distribution restricted to U.S. government agencies).
Top

Renjifo, C., Barsic, D., Carmen, C., Norman, K., and Peacock, G.S. (2008),

"Improving Radial Basis Function Kernel Classification Through Incremental Learning and Automatic Parameter Selection,"

Neurocomputing, vol. 72, pp. 3−14 (support vector machine that employs a greedy search across the training data to select the basis vectors of classifier and tunes parameters using SPSA).

Top

Renotte, C., Vande Wouwer, A., and Remy, M. (2000),

"Neural Modeling and Control of a Heat Exchanger Based on SPSA Techniques,"
Proceedings of the American Control Conference, 28-30 June 2000, Chicago, IL, pp. 3299-3303 (control application).
Top

Rezayat, F. (1995),

"On the Use of an SPSA-based Model-Free Controller in Quality Improvement,"
Automatica, vol. 31, pp. 913-915 (operations design and process quality control).
Top

Rezayat, F. (1999),

"Constrained SPSA Controller for Operations Processes,"
IEEE Transactions on Systems, Man, and Cybernetics — A, vol. 29, pp. 645-649 (applies penalty functions to implement constrained SPSA controller in business application of quality improvement).
Top

Sadegh, P. (1997),

"Constrained Optimization via Stochastic Approximation with a Simultaneous Perturbation Gradient Approximation,"
Automatica, vol. 33, pp. 889-892 (constrained optimization via Kuhn-Tucker considerations).
Top

Sadegh, P. and Spall, J.C. (1997),

"Optimal Random Perturbations for Stochastic Approximation Using a Simultaneous Perturbation Gradient Approximation,"
Proceedings of the American Control Conference, 4-6 June 1997, Albuquerque, NM, pp. 3582-3586 (guidelines for optimally choosing the distribution of the simultaneous perturbation vector).
Top

Sadegh, P. and Spall, J.C. (1998),

"Optimal Sensor Configuration for Complex Systems,"
Proceedings of the American Control Conference, 24-26 June 1998, Philadelphia, PA, pp. 3575-3579 (locating and adjusting sensors for maximizing useful information about an object or process).
Top

Schwartz, J.D. and Rivera, D.E. (2006),

"Simulation-Based Optimal Tuning of Model Predictive Control Policies for Supply Chain Management using Simultaneous Perturbation Stochastic Approximation,"

Proceedings of the American Control Conference, 14-16 June 2006, Minneapolis, MN, pp. 556-561 (paper WeA16.4) (model-predictive control as the basis for inventory management policy for supply chains).

Top

Schwartz, J.D., Rivera, D.E., and Kempf, K.G. (2005),

“Towards Control-Relevant Forecasting in Supply Chain Management,”
Proceedings of the American Control Conference, 8–10 June 2005, Portland, OR, pp. 202–207 (application to building controller to compensate for demand forecast error in a manufacturing supply chain).
Top

Schwartz, J.D., Wang, W., and Rivera, D.E. (2006),

"Simulation-Based Optimization of Process Control Policies for Inventory Management in Supply Chains,"

Automatica, vol. 42(8), pp. 1311-1320 (optimal control applications to management science).

Top

Sethares, W.A. (2002),

"Real-Time Adaptive Tunings Using MAX,"
Journal of New Music Research, vol. 31, pp. 347–355 (adaptive tuning algorithm to change the pitch of musical notes in real time).
Top

Seyedpoora, S.M., Salajeghehb, J., Salajeghehb, E., and Gholizadehc, S. (2011),

"Optimal Design of Arch Dams Subjected to Earthquake Loading by a Combination of Simultaneous Perturbation Stochastic Approximation and Particle Swarm Algorithms,"

Applied Soft Computing, vol. 11(1), pp, 39−48 (combination of SPSA and particle swarm optimization [PSO] algorithms for finding optimal shapes of arch dams considering fluid–structure interaction subject to earthquake loading).

Top

Shara, N.M. and Flournoy, N. (2006),

"Multivariate Optimizing Up-and-Down Designs,"

Paper presented at Georgetown University, Lombardi Comprehensive Cancer Center, Washington, DC, 15 September 2006 (determining optimal doses for multiple drugs).

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Shekar, S., Smith, A.J., Menz, W.J., Sander, M., and Kraft, M. (2012),

"A Multidimensional Population Balance Model to Describe the Aerosol Synthesis of Silica Nanoparticles,"

Journal of Aerosol Science, vol. 44, pp. 83–98 (presents population balance model to describe the aerosol synthesis of silica nanoparticles from tetraethoxysilane; free parameters estimated by fitting model response to experimental values of collision and primary particle diameters using low discrepancy Sobol sequences followed by SPSA).

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Sidorov, K., Richmond, S., and Marshall, D. (2009),

"An Efficient Stochastic Approach to Groupwise Non-rigid Image Registration,"

Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA, June 2009, pp. 2208−2213 (search for optimal group-wise dense correspondence in large sets of unmarked images).

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Song, J., Xu, Y., Yam, Y., and Nechyba, M.C. (1998),

"Optimization of Human Control Strategy with Simultaneously Perturbed Stochastic Approximation,"
Proceedings of the IEEE Conference on Intelligent Robots and Systems, Part 2, pp. 983-988 (controllers involving human-machine interaction).
Top

Song, Q., Spall, J.C., and Soh, Y.C. (2003),

"Robust Neural Network Tracking Controller Using Simultaneous Perturbation Stochastic Approximation,"
Proceedings of the IEEE Conference on Decision and Control, 9-12 December 2003, Maui, Hawaii, pp. 6194-6199 (model-free controller with guaranteed stability in closed loop).
Top

Song, Q., Spall, J.C., and Soh, Y.C. (2008),

"Robust Neural Network Tracking Controller Using Simultaneous Perturbation Stochastic Approximation,"

IEEE Transactions on Neural Networks, vol. 19(5), pp. 817-835 (conic sector theory and SPSA to establish robust neural control system for nonlinear systems).

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Spall, J.C. (1987),

"A Stochastic Approximation Technique for Generating Maximum Likelihood Parameter Estimates,"
Proceedings of the American Control Conference, 10-12 June 1987, Minneapolis, MN, pp. 1161-1167 (early paper on SPSA).
Top

Spall, J.C. (1992),

"Multivariate Stochastic Approximation Using a Simultaneous Perturbation Gradient Approximation,"
IEEE Transactions on Automatic Control, vol. 37, pp. 332-341 (core paper on theoretical and numerical properties).
Top

Spall, J.C. (1994),

"Developments in Stochastic Optimization Algorithms with Gradient Approximations Based on Function Measurements,"
Proceedings of the Winter Simulation Conference (J.D. Tew, M.S. Manivannan, D.A. Sadowski, and A.F. Seila, eds.) pp. 207-214 (review of several approaches in gradient-free setting).
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Spall, J.C. (1997),

"A One-Measurement Form of Simultaneous Perturbation Stochastic Approximation,"
Automatica, vol. 33, pp. 109-112 (uses gradient estimate based on only one function measurement with potential applications with time-varying loss functions).
Top

Spall, J.C. (1998),

"Implementation of the Simultaneous Perturbation Algorithm for Stochastic Optimization,"
IEEE Transactions on Aerospace and Electronic Systems, vol. 34, pp. 817-823 (guidelines for practical implementation and choice of gain coefficients).
Top

Spall, J.C. (1998),

"An Overview of the Simultaneous Perturbation Method for Efficient Optimization,"
Johns Hopkins APL Technical Digest, vol. 19, pp. 482-492 (survey paper on SPSA).
Top

Spall, J.C. (2000),

"Adaptive Stochastic Approximation by the Simultaneous Perturbation Method,"
IEEE Transactions on Automatic Control, vol. 45, pp. 1839-1853 (gradient-free and gradient-based methods for obtaining near-optimal or optimal convergence rates via stochastic analogues to deterministic Newton-Raphson algorithm; Hessian matrix estimation).
Top

Spall, J.C. (2005),

"Monte Carlo Computation of the Fisher Information Matrix in Nonstandard Settings,"
Journal of Computational and Graphical Statistics (American Statistical Assoc.), vol. 14(4), pp. 889-909 (use of simultaneous perturbation-based Hessian estimation for easy calculation of the Fisher information matrix [as appears, e.g., in Cramér-Rao bound] in general nonlinear problems, including analysis of antithetic random numbers).
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Spall, J.C. (2009),

"Feedback and Weighting Mechanisms for Improving Jacobian Estimates in the Adaptive Simultaneous Perturbation Algorithm,"

IEEE Transactions on Automatic Control, vol. 54(6), pp. 1216-1229 (method for improving the Jacobian [or Hessian] estimate in the adaptive SPSA method).

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Spall, J.C and Chin, D.C. (1997),

"Traffic-Responsive Signal Timing for System-Wide Traffic Control,"
Transportation Research—Part C, vol. 5, pp. 153-163 (use in building signal-timing control algorithm for traffic network).
Top

Spall, J.C. and Cristion, J.A. (1994),

"Nonlinear Adaptive Control Using Neural Networks: Estimation With a Smoothed Form of Simultaneous Perturbation Gradient Approximation,"
Statistica Sinica, vol. 4, pp. 1- 27 (adaptive control and "smoothed" gradient approximation).
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Spall, J.C. and Cristion, J.A. (1997),

"A Neural Network Controller for Systems with Unmodeled Dynamics with Applications to Wastewater Treatment,"
IEEE Transactions on Systems, Man, and Cybernetics—B, vol. 27, pp. 369- 375 (control application and neural network training).
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Spall, J.C. and Cristion, J.A. (1998),

"Model-Free Control of Nonlinear Stochastic Systems with Discrete-Time Measurements,"
IEEE Transactions on Automatic Control, vol. 43, pp. 1198-1210 (establishes formal convergence of SPSA-based controller; also illustrates numerical properties).
Top

Spall, J.C., Hill, S.D., and Stark, D.R. (2006),

"Theoretical Framework for Comparing Several Stochastic Optimization Approaches,"
in Probabilistic and Randomized Methods for Design under Uncertainty (G. Calafiore and F. Dabbene, eds.), Springer, Berlin, Chapter 3 (some comparative theoretical insight into random search, SPSA, simulated annealing, and evolution strategies).
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Srinivasan, D., Choy, M.C., and Cheu, R.L. (2006),

"Neural Networks for Real-Time Traffic Signal Control,"

IEEE Transactions on Intelligent Transportation Systems, vol. 7(3), pp. 261-272 (distributed multiagent control for urban traffic management and signal timing).

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Steenis, R. and Rivera, D.E. (2011),

“Plant-Friendly Signal Generation for System Identification Using a Modified Simultaneous Perturbation Stochastic Approximation (SPSA) Methodology,”

IEEE Transactions on Control Systems Technology, vol. 19(6), pp.1604−1612 (considers modified SPSA version [MSPSA-K] that perturbs signal phase parameters in K subsets rather than simultaneously; method can be applied to signals with arbitrarily defined spectrum in both amplitude and frequency).

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Sultan, I. and Puthiyaveettil, P. (2012),

“Calibration of an Articulated CMM Using Stochastic Approximations,”

The International Journal of Advanced Manufacturing Technology, published online 17 January 2012 (coordinate measuring machine: identify the parameters of the kinematic model in order for the accurate performance to be achieved; demonstration on five-axis revolute-joint serial manipulator robot with high degree of dexterity).

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Sultan, I.A. and Schaller C.G. (2011),

"Optimum Positioning of Ports in the Limaçon Gas Expanders,"

Journal of Engineering for Gas Turbines and Power—Transactions of the ASME, vol. 133(10), pp. 103002-1 − 103002-11 (positive displacement expanders are used in the fields of micropower generation and refrigeration engineering; SPSA is used to find the locations for the expander ports that produce the best expander performance).

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Sun, C., Hirata, A., Ohira, T., Karmakar, N.C. (2004),

"Fast Beamforming of Electronically Steerable Parasitic Array Radiator Antennas: Theory and Experiment,"
IEEE Transactions on Antennas and Propagation, vol. 52(7), pp. 1819–1832 (optimal beamforming in antenna design).
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Taflanidis, A. A. and Beck, J. L. (2008),

"An Efficient Framework for Optimal Robust Stochastic System Design Using Stochastic Simulation,"

Computer Methods in Applied Mechanics and Engineering, vol. 198, pp. 88–101 (provides details on how stochastic subset optimization can be efficiently combined with SPSA for controller design; example in optimization of base-isolation system for three-story structure in face of earthquake excitations).

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Tayong, H., Beasley, A., Cole-Rhodes, A., and Cooper, A.B. (2002),

"Adaptive Optimization of a Parametric Receiver for Fast Frequency-Hopping,"
36th Annual Conference on Information Sciences and Systems, 20-22 March 2002, Princeton, NJ, paper no. 180 (CD-ROM Proceedings) (signal processing application).
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Thangavel, P. and Kathirvalavakumar, T. (2003),

"Simultaneous Perturbation for Single Hidden Layer Networks—Cascade Learning,"
Neurocomputing, vol. 50, pp. 193–209 (training in single layer neural networks).
Top

Ting, C.-J. and Schonfeld, P. (1998),

"Optimization through Simulation of Waterway Transportation Investments,"
Transportation Research Record, no. 1620, pp. 11- 16 (example of simulation-based optimization for long-term planning of waterway network capacity).
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Tsakalaki, E.P. Alrabadi, O.N. Papadias, C.B. Prasad, R. (2011),

"Adaptive Reactance-Controlled Antenna Systems for Multi-Input Multi-Output Applications,"

IET Microwaves, Antennas and Propagation, vol. 5(8), pp. 975−984 (considers multi-input multi-output systems under real-life effects of spatial correlation and antenna mutual coupling; aim to maximize the communication rate).

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Vakhitov, A.T., Granichin, O.N., and Sysoev, S.S. (2006),

"A Randomized Stochastic Optimization Algorithm: Its Estimation Accuracy,"

Automation and Remote Control, vol. 67(4), pp. 589-597 (possible SPSA implementation in quantum computing).

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Vande Wouwer, A. and Renotte, C. (2003),

"Stochastic Approximation Techniques Applied to Parameter Estimation in a Biological Model,"
Proceedings of the IEEE Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Lviv, Ukraine, 9-11 September 2003 (nonlinear system identification for biological models).
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Vande Wouwer, A., Renotte, C., and Remy, M. (1999),

"On the Use of Simultaneous Perturbation Stochastic Approximation for Neural Network Training,"
Proceedings of the American Control Conference, 2-4 June 1999, San Diego, CA, pp. 388-392 (experiments with first- and second-order SPSA).
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Vande Wouwer, A., Renotte, C., and Remy, M. (2003),

"Application of Stochastic Approximation Techniques in Neural Modelling and Control,"
International Journal of Systems Science, vol. 34, pp. 851–863 (modifications of basic algorithm for use in neural network training).
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Vande Wouwer, A., Renotte, C., Bogaerts, Ph., and Remy, M. (2001),

"Application of SPSA Techniques in Nonlinear System Identification,"
European Control Conference, 4-7 September 2001, Porto, Portugal (SPSA for control of bioprocesses).
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Vande Wouwer, A., Renotte, C., and Bogaerts, P.H. (2006),

"A Short Note on SPSA Techniques and Their use in Nonlinear Bioprocess Identification,"

Mathematical and Computer Modelling of Dynamical Systems, vol.12(5), pp. 415–422 (considers use of adaptive gain sequences, illustrating approach in context of a bioprocess model describing the evolution of batch animal cell cultures).

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Vaze, V., Antoniou, C., Wen, Y., and Ben-Akiva, M. (2009),

"Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance,"

Transportation Research Record: Journal of the Transportation Research Board, No. 2090, pp. 1–9 (comparison of SPSA and GA in calibration of demand and supply simulators for dynamic traffic assignment for use in providing consistent travel information and in efficient traffic management).

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Velusamy, S., Bhatnagar, S., Basavaraja, S.V., and Sridhar, V. (2008),

"SPSA Based Feature Relevance Estimation for Video Retrieval,"

Proceedings of the IEEE 10th Workshop on Multimedia Signal Processing (MMSP), 8−10 October 2008, Cairns, Qld, Australia, pp. 598−603 (interactive video retrieval with efficient indexing from features such as color, texture, face, and audio information; optimizes feature weights in content-based video retrieval).

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Venkatesh, Y.V., Kassim, A.A., and Zonoobi, D. (2010),

"Medical Image Reconstruction from Sparse Samples using Simultaneous Perturbation Stochastic Optimization,"

17th IEEE International Conference on Image Processing (ICIP), 26−29 Sept. 2010, Hong Kong, pp. 3369−3372 (approach for Lp-norm [p < 1] reconstruction of medical images from compressive samples in either the spatial or transformed domain; demonstration on real and simulated images).

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Vitsentiy, V. (2002),

"Improvement of Human-Machine Interaction with Applications to Information Retrieval System,"
Proceedings of the First International IEEE Symposium on Intelligent Systems, 10-12 September 2002, Varna, Bulgaria (adaptive control involving human-machine interaction).
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Vorontsov, M.A., Carhart, G.W., Cohen, M., and Cauwenberghs, G. (2000),

"Adaptive Optics Based on Analog Parallel Stochastic Optimization: Analysis and Experimental Demonstration,"
Journal of the Optical Society of America A, vol. 17, pp. 1440-1453 (application in adaptive optical-based control system).
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Vorontsov, M.A. and Carhart, G.W. (2006),

"Adaptive Wavefront Control with Asynchronous Stochastic Parallel Gradient Descent Clusters,"

Journal of the Optical Society of America A, vol. 23(10), pp. 2613–2622 (shows that division of controls into asynchronous clusters improves the system performance in an adaptive optics system).

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Wang, I.-J. and Chong, E.K.P. (1996),

"A Deterministic Analysis of Simultaneous Perturbation Stochastic Approximation,"
Proceedings of the 30th Conference on Information Sciences and Systems, pp. 918-922 (convergence analysis via deterministic methods).
Top

Wang, I.-J. and Chong, E.K.P. (1998),

"A Deterministic Analysis of Stochastic Approximation with Randomized Directions,"
IEEE Transactions on Automatic Control, vol. 43, pp. 1745-1749 (convergence analysis of SPSA and random directions SA via deterministic methods).
Top

Wang, I.-J. and Spall, J.C. (2003),

"Stochastic Optimization with Inequality Constraints Using Simultaneous Perturbations and Penalty Functions,"
Proceedings of the IEEE Conference on Decision and Control, 9-12 December 2003, Maui, Hawaii, pp. 3808-3813 (method for handling general constraints; includes convergence and asymptotic distribution theory).
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Wang, I.-J. and Spall, J.C. (2008),

"Stochastic Optimization with Inequality Constraints Using Simultaneous Perturbations and Penalty Functions,"

International Journal of Control, vol. 81(8), pp. 1232-1238 (updated version of above 2003 CDC paper; method for handling general constraints; includes convergence analysis, asymptotic distribution theory, and numerical example).

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Wang, L. and Prabhu, V. (2009),

"A Stochastic Perturbation Algorithm for Inventory Optimization in Supply Chains,"

International Journal of Information Systems and Supply Chain Management, vol. 2(3), pp. 1-18 (considers an augmented SPSA to include ordinal optimization, non-uniform gain, and line search; comparison with genetic algorithm).

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Wang, Q. and Spall, J.C. (2011),

“Discrete Simultaneous Perturbation Stochastic Approximation on Loss Functions with Noisy Measurements,”

Proceedings of the American Control Conference, 29 June-1 July 2011, San Francisco, CA, pp. 4520−4525 (paper FrB10.3) (method for optimization of discrete functions in the presence of noisy function measurements).

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Webster, T.M. (1988),

"Some Experience with Stochastic Approximation Algorithms in Large-Scale Systems,"
Proceedings of the American Statistical Association, Statistical Computing Section, August 1988, New Orleans, LA, pp. 181-186 (some early numerical experiments).
Top

Weyrauch, T. and Vorontsov, M.A. (2002),

"Dynamic Wave-Front Distortion Compensation with a 134-Control-Channel Submillisecond Adaptive System,"
Optics Letters, vol. 27, pp. 751-753 (application in adaptive optics system).
Top

Whitney, J.E. and Hill, S.D. (2001),

"Constrained Optimization Over Discrete Sets via SPSA with Application to Non-Separable Resource Allocation,"
Proceedings of the Winter Simulation Conference, 9-12 December 2001, Arlington, VA, pp. 313-317 (application of SPSA in discrete optimization).
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Whitney, J.E., Duncan, K., Richardson, M., and Bankman, I. (2000),

"Parameter Estimation in a Highly Nonlinear Model Using Simultaneous Perturbation Stochastic Approximation,"
Communications in Statistics—Theory and Methods, vol. 29, pp. 1247-1256 (use of first- and second-order SPSA in a physical model).
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Xing, X.Q. and Damodaran, M. (2002),

"Assessment of Simultaneous Perturbation Stochastic Approximation Method for Wing Design Optimization,"
Journal of Aircraft (AIAA), vol. 39, pp. 379-381 (comparison with simulated annealing and genetic algorithms on an aircraft design problem).
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Xing, X.Q. and Damodaran, M. (2005),

"Application of Simultaneous Perturbation Stochastic Approximation Method for Aerodynamic Shape Design Optimization,"
AIAA Journal, vol. 43, pp. 284–294 (SPSA is compared with simulated annealing for a class of aerodynamic design optimization problems).
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Xing, X.Q. and Damodaran, M. (2005),

"Inverse Design of Transonic Airfoils Using Parallel Simultaneous Perturbation Stochastic Approximation,"
Journal of Aircraft (AIAA), vol. 42(2), pp. 568–570 (a parallel version of SPSA to deal with inverse airfoil design problem).
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Xiong, X., Wang, I.-J. and Fu, M.C. (2002),

"An Asymptotic Analysis of Stochastic Approximation with Deterministic Perturbation Sequences,"
Proceedings of the Winter Simulation Conference, 8-11 December 2002, San Diego, CA, pp. 285-291 (use of deterministic—vs. random—perturbation vectors).
Top

Xu, P., Li, G., and Wang, K. (2012),

"Self Tuning of PID Controller Based on Simultaneous Perturbation Stochastic Approximation,"
in Advances in Electronic Engineering, Communication and Management Vol.1 (D. Jin and S. Lin, eds.), Lecture Notes in Electrical Engineering 139, pp. 647–652 (online tuning of parameters of a PID controller without assuming model).
Top

Xu, Y., Song, J., Nechyba, M.C., Yam, Y. (2002),

"Performance Evaluation and Optimization of Human Control Strategy," Robotics and Autonomous Systems, vol. 39(1), pp. 19-36 (controllers involving human-machine interaction).
Top

Yakovlev, V. and Tempea, G. (2002),

"Optimization of Chirped Mirrors,"
Applied Optics—LP, vol. 41, pp. 6514-6520 (optimization for the design of dielectric multilayer mirrors).
Top

Yang, J.S. (2004),

"Traffic Signal Timing Control for a Small-Scale Road Network,"

Proceedings of the 6th IASTED International Conference on Control and Applications, 1-3 March 2004, Marina Del Rey (Los Angeles), California, USA, pp. 117-122 (paper 441-048) (pilot study of the development and evaluation of a traffic signal timing control for a small scale road network in downtown Duluth, Minnesota).

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Yang, J.S. (2008),

"An Optimization-Based Approach to Special-Events Traffic Signal Timing Control,"

Control and Intelligent Systems, vol. 36(2), paper no. 201-1690 (SPSA and neural nets for signal control in the face of a sudden traffic surge immediately after special events such as conventions, sporting events, or concerts).

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Yin, G., Zhang, Q., Yan, H.M., and Boukas, E.K. (2001),

"Random Direction Optimization Algorithms with Applications to Threshold Controls,"
Journal of Optimization Theory and Applications, vol. 110, pp. 211-233 (application in a constrained optimal control problem).
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Yuan. Q.H. (2008),

"A Model Free Automatic Tuning Method for a Restricted Structured Controller by Using Simultaneous Perturbation Stochastic Approximation (SPSA),"

Proceedings of the American Control Conference, 11-13 June 2008, Seattle, Washington, USA, pp. 1539-1545 (paper WeC09.5) (automatic tuning of PID controllers, with example in electro-hydraulic valve).

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Yue, X. (2008),

"Improved Simultaneous Perturbation Stochastic Approximation and its Application in Reinforcement Learning,"

Proceedings of the 2008 International Conference on Computer Science and Software Engineering, vol. 1, pp. 329−332, 12−14 December 2008 (uses nonlinear conjugate gradient and reinforcement learning method to improve SPSA convergence).

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Yue, Y. and Burges, C. J. C. (2007),

"On Using Simultaneous Perturbation Stochastic Approximation for IR Measures, and the Empirical Optimality of LambdaRank,"

NIPS Machine Learning for Web Search Workshop, 7 December 2007, Whistler, Canada (investigation of whether gradient of information retrieval performance measures, such as normalized discounted cumulative gain, might be numerically approximated given discrete nature of objective function).

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Zein, S., Canot, E., Erhel, J., and Nassif, N. (2008),

"Determination of the Mechanical Properties of a Solid Elastic Medium from a Seismic Wave Propagation Using Two Statistical Estimators,"

Mathematics and Mechanics of Solids, vol. 13(5), pp. 388-407 (estimation of mechanical parameters for inverse problem consisting in the determination of the mechanical properties of a layered solid elastic medium in contact with a fluid medium; comparison with MCMC).

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Zhang, J., Zhao, R., and Tang, W. (2008),

"Fuzzy Age-Dependent Replacement Policy and SPSA Algorithm Based on Fuzzy Simulation,"

Information Sciences: An International Journal, vol. 178(2), pp. 573-583 (estimation for problem of age-dependent replacement policy in maintenance using a fuzzy simulation technique to estimate the expected value of the objective function).

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Zhao, H., Li, Y., Yao, J., and Zhang, K. (2011),

“Theoretical Research on Reservoir Closed-Loop Production Management,”

Science in China Series E: Technological Sciences, vol. 54(10), pp. 2815−2824 (reservoir closed-loop production management; demonstrated reduced geological uncertainty and provided reasonable estimate of reservoir model without calculation of adjoint-gradient).

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Zhou, Y.-L., Zhang, Q.Z., Li, X.-D., Gan, W.-S. (2008),

"On the use of an SPSA-based model-free feedback controller in active noise control for periodic disturbances in a duct,"

Journal of Sound and Vibration, vol. 317, pp. 456-472 (feedback active noise control system using a model-free controller).

Zhu, X. (2001),

"Matrix Conditioning and Adaptive Simultaneous Perturbation Stochastic Approximation Method,"
Proceedings of the American Control Conference, 25-27 June 2001, Arlington, VA, pp. 1389-1395 (modified form of adaptive ["second-order"] SPSA).
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Zhu, X. and Spall, J.C. (2002),

"A Modified Second-Order SPSA Optimization Algorithm for Finite Samples,"

International Journal of Adaptive Control and Signal Processing, vol. 16, pp. 397-409 (modified form of adaptive [“second-order”] SPSA).
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Zonoobi, D., Kassim, A.A., Venkatesh, Y.V. (2011),

"Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples,"

IEEE Journal of Selected Topics in Signal Processing, vol. 5(5), pp. 927−932 (compressive sensing; reconstruction of a discrete signal from a set of incomplete observations using Gini index as measure of sparsity).

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