Introduction to stochastic search and optimization - estimation, simulation, and control

By James C. Spall. 2003

1,402 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Mathematical Optimization, Signal Processing

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Citations in the corpus

Variational Inference: A Review for Statisticians (2016, background): “This idea has blossomed (Kushner and Yin 1997; Spall 2003).”
Reinforcement learning in robotics: A survey (2013, methodology): “Black box methods are general stochastic optimization algorithms (Spall, 2003) using only the expected return of policies, estimated by sampling, and do not leverage any of the internal structure of the reinforcement learning problem.”
Empirically Evaluating Multiagent Learning Algorithms (2014, methodology): “We also tested the Global Stochastic Approximation algorithm, GSA , of Spall (2003); see Table VII.”
ParamILS: An Automatic Algorithm Configuration Framework (2014, background): “Algorithm conﬁguration is a stochastic optimization problem, and there exists a large body of algorithms designed for such problems (see, e.g., Spall, 2003).”
A new optimization approach for source-encoding full-waveform inversion (2013, methodology): “This is done by either (1) averaging over all the past updated models, (2) averaging over past gradients, or (3) averaging the Hessian operator (Goldberg, 1989; Spall, 1992, 2003; Hoos and Stutzle, 2004).”
Inference for nonlinear dynamical systems (2006, background): “Heuristically, can be thought of as a ‘‘cooling’’ parameter, analogous to that used in simulated annealing (39).”
A survey on approaches for reliability-based optimization (2010, methodology): “(2007); Goldberg (1989); Haftka and Gürdal (1992); Nocedal and Wright (1999); Spall (2003).”
A Multi-Facet Survey on Memetic Computation (2011, methodology): “The refinement procedure used in hybrids, on the other hand, comes in the form of both deterministic [70] and stochastic [71].”
Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms (2006, background): “Stochastic and non-gradient search methods [11] might be considered in order to avoid the computational complexity of the combinatorial optimization methods described above and thus speed up the convergence to a good feasible solution.”
Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments (2014, background): “The many advantages of using stochastic gradient descent algorithms, like the simultaneous perturbation stochastic approximation algorithm [54], to approach a sensor-based deployment problem have already been highlighted in [55].”
Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback. (2010, methodology): “Similar schemes for constructing gradient estimators using multiple function evaluations have also been analyzed in the framework of stochastic optimization (Polyak & Tsypkin, 1973; Spall, 2003) and derivative-free optimization (Conn et al., 2009).”
Sampling-Based Path Planning on Configuration-Space Costmaps (2010, background): “Developed in order to find global optima in very complex spaces [16], these techniques introduce randomness as a means to avoid local minima traps.”