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Related Experiment Video

Updated: Jul 7, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Comparative study of stochastic algorithms for system optimization based on gradient approximations.

¹Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|January 1, 1997

Summary

This study compares three stochastic approximation (SA) algorithms for optimization with noisy data. Simultaneous Perturbation SA (SPSA) is found to be the most effective algorithm for these challenging problems.

Related Experiment Videos

Last Updated: Jul 7, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

Optimization and Control Theory
Machine Learning
Statistical Inference

Background:

Stochastic approximation (SA) algorithms are crucial for system optimization when only noisy measurements are available, lacking direct gradient information.
These methods are widely applied in adaptive control, neural network training, experimental design, and stochastic optimization.

Purpose of the Study:

To analyze and compare three types of SA algorithms: finite-difference SA (FDSA), random directions SA (RDSA), and simultaneous-perturbation SA (SPSA).
To evaluate their performance in a multivariate Kiefer-Wolfowitz setting using only noisy loss function measurements.

Main Methods:

The study examines algorithms in a Kiefer-Wolfowitz setting, relying solely on noisy loss function measurements.
It analyzes the asymptotic error distribution for RDSA algorithms.
Theoretical comparison using mean-square errors and numerical evaluations are conducted for RDSA, SPSA, and FDSA.

Main Results:

RDSA and SPSA utilize randomized gradient approximations, requiring fewer function measurements per iteration compared to FDSA.
Asymptotic error distributions for RDSA algorithms are derived.
Both theoretical and numerical analyses indicate SPSA's superior performance.

Conclusions:

Simultaneous Perturbation SA (SPSA) is identified as the preferable algorithm among the studied methods.
The findings provide valuable insights for selecting appropriate SA algorithms in noisy optimization scenarios.