Discrete simultaneous perturbation stochastic approximation on loss function with noisy measurements

Wang, Qi; Spall, James C.

doi:10.1109/acc.2011.5991407

Cited by 28 publications

(33 citation statements)

References 74 publications

(161 reference statements)

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“…2 }+f 2 and the inequality in the case when 0 < b 1 < L 1 + 1 is because that, by a similar approach as in (32), (b 2 , g 1 , a 2 ) because of the convexity of h i and the condition P e (g 1 ) ≥ P e (g 1 + 1). By knowing the submodularity of a 2 ) and (b 2 , g 1 ).…”

Section: Appendix Amentioning

confidence: 86%

“…The advantage of formulating problem (24) is that the solutions can be approximated by the DSPSA algorithm [32] presented in Algorithm 1. The parameters/functions in this algorithm are explained as follows…”

Section: Discrete Simultaneous Perturbation Stochastic Approximationmentioning

confidence: 99%

“…Algorithm 1: DSPSA [32] input : initial guess φ (0) , total number of iterations N, step size parameters A, B and α output: [ φ (N) ], the closest integer point to φ (N) by Euclidean distance.…”

Section: Discrete Simultaneous Perturbation Stochastic Approximationmentioning

confidence: 99%

“…Therefore, if DSPSA can be applied when Theorem 4.12 holds, it can be also applied when Theorem 4.13 and 4.15 hold. 9 The gradient d in (27) is defined based on the discrete mid-point convexity [32]. 10 More comparisons of far-sighted and myopic policies in NC-TWRC are presented in [13].…”

Section: Endnotesmentioning

confidence: 99%

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Structured optimal transmission control in network-coded two-way relay channels

Ding

Sadeghi

Kennedy

2015

J Wireless Com Network

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This paper considers a transmission control problem in network-coded two-way relay channels (NC-TWRC), where the relay buffers randomly arrived packets from two users, and the channels are assumed to be fading. The problem is modeled by a discounted infinite horizon Markov decision process (MDP). The objective is to find an adaptive transmission control policy that minimizes the packet delay, buffer overflow, transmission power consumption and downlink error rate simultaneously and in the long run. By using the concepts of submodularity, multimodularity and L -convexity, we study the structure of the optimal policy searched by dynamic programming (DP) algorithm. We show that the optimal transmission policy is nondecreasing in queue occupancies and/or channel states under certain conditions such as the chosen values of parameters in the MDP model, channel modeling method, and the preservation of stochastic dominance in the transitions of system states. Based one these results, we propose to use two low-complexity algorithms for searching the optimal monotonic policy: monotonic policy iteration (MPI) and discrete simultaneous perturbation stochastic approximation (DSPSA). We show that MPI reduces the time complexity of DP, and DSPSA is able to adaptively track the optimal policy when the statistics of the packet arrival processes change with time.

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Section: Appendix Amentioning

confidence: 86%

Section: Discrete Simultaneous Perturbation Stochastic Approximationmentioning

confidence: 99%

Section: Discrete Simultaneous Perturbation Stochastic Approximationmentioning

confidence: 99%

Section: Endnotesmentioning

confidence: 99%

See 2 more Smart Citations

Structured optimal transmission control in network-coded two-way relay channels

Ding

Sadeghi

Kennedy

2015

J Wireless Com Network

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“…i ← i + 1 6: end while Recent extensions of the SPSA method include introducing a global search component to the algorithm by injecting Monte Carlo noise during the update step [134], and using it to solve combined discrete/continuous optimization problems [199]. Recent work also addresses improving Jacobian as well as Hessian estimates in the context of the SPSA algorithm [187].…”

mentioning

confidence: 99%

Simulation optimization: a review of algorithms and applications

et al. 2014

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Simulation Optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation-discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise-various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in simulation optimization as compared to mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.

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Simulation optimization: a review of algorithms and applications

et al. 2015

View full text Add to dashboard Cite

Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation-discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise-various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in SO as compared to algebraic model-based mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.Keywords Simulation optimization · Optimization via simulation · Derivative-free optimization This is an updated version of the paper that appeared in 4OR, 12(4), 301-333 (2014).

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Discrete simultaneous perturbation stochastic approximation on loss function with noisy measurements

Cited by 28 publications

References 74 publications

Structured optimal transmission control in network-coded two-way relay channels

Structured optimal transmission control in network-coded two-way relay channels

Simulation optimization: a review of algorithms and applications

Simulation optimization: a review of algorithms and applications

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