Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
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This paper presents the result for Simultaneous Perturbation Stochastic Approximation (SPSA) on the BBOB 2010 noiseless testbed. SPSA is a stochastic gradient approximation strategy which uses random directions for the gradient estimate. The paper describes the steps performed by the strategy and the experimental setup. The chosen setup represents a rather basic variant of SPSA. Overall the strategy is able to solve 2 of the 24 test functions. For each test function at least one target level was reached for D = 3.