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Quasi-stochastic approximation: Design principles with applications to extremum seeking control
How can you optimize a function based on evaluations of this function without access to its
gradient? Kiefer and Wolfowitz proposed a solution in the early 1950s based on stochastic …
gradient? Kiefer and Wolfowitz proposed a solution in the early 1950s based on stochastic …
Approaching quartic convergence rates for quasi-stochastic approximation with application to gradient-free optimization
Stochastic approximation is a foundation for many algorithms found in machine learning and
optimization. It is in general slow to converge: the mean square error vanishes as $ O (n …
optimization. It is in general slow to converge: the mean square error vanishes as $ O (n …
Markovian Foundations for Quasi-Stochastic Approximation
This paper concerns quasi-stochastic approximation (QSA) to solve root finding problems
commonly found in applications to optimization and reinforcement learning. Theory is …
commonly found in applications to optimization and reinforcement learning. Theory is …
Markovian foundations for quasi-stochastic approximation with applications to extremum seeking control
This paper concerns quasi-stochastic approximation (QSA) to solve root finding problems
commonly found in applications to optimization and reinforcement learning. The general …
commonly found in applications to optimization and reinforcement learning. The general …
Extremely fast convergence rates for extremum seeking control with Polyak-Ruppert averaging
Stochastic approximation is a foundation for many algorithms found in machine learning and
optimization. It is in general slow to converge: the mean square error vanishes as $ O (n …
optimization. It is in general slow to converge: the mean square error vanishes as $ O (n …