Results 221 to 230 of about 67,878 (263)
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Optimization by Stochastic Continuation
SIAM Journal on Imaging Sciences, 2010Simulated annealing (SA) and deterministic continuation are well-known generic approaches to global optimization. Deterministic continuation is computationally attractive but produces suboptimal solutions, whereas SA is asymptotically optimal but converges very slowly.
Marc C. Robini, Isabelle E. Magnin
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On fuzzy stochastic optimization
Fuzzy Sets and Systems, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
M. K. Luhandjula, Madan M. Gupta
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Stochastic program optimization
Communications of the ACM, 2016The optimization of short sequences of loop-free, fixed-point assembly code sequences is an important problem in high-performance computing. However, the competing constraints of transformation correctness and performance improvement often force even special purpose compilers to produce sub-optimal code.
Eric Schkufza +2 more
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Adaptive Stochastic Optimization: A Framework for Analyzing Stochastic Optimization Algorithms
IEEE Signal Processing Magazine, 2020Optimization lies at the heart of machine learning (ML) and signal processing (SP). Contemporary approaches based on the stochastic gradient (SG) method are nonadaptive in the sense that their implementation employs prescribed parameter values that need to be tuned for each application.
Frank E. Curtis, Katya Scheinberg
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Stochastic Discrete Optimization
SIAM Journal on Control and Optimization, 1992Summary: A stochastic search method is proposed for finding a global solution to the stochastic discrete optimization problem in which the objective function must be estimated by Monte Carlo simulation. Although there are many practical problems of this type in the fields of manufacturing engineering, operations research, and management science, there ...
Yan, Di, Mukai, H.
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Dynamic stochastic optimization
53rd IEEE Conference on Decision and Control, 2014A framework for sequentially solving stochastic optimization problems with stochastic gradient descent is introduced. Two tracking criteria are considered, one based on being accurate with respect to the mean trajectory and the other based on being accurate in high probability (IHP).
Craig Wilson +2 more
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Stochastic Adaptive Optimization With Dithers
IEEE Transactions on Automatic Control, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Siyu Xie +4 more
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BLACKWELL OPTIMALITY IN STOCHASTIC GAMES [PDF]
International Game Theory Review, 2013 Blackwell optimality in a finite state-action discounted Markov decision process (MDP) gives an optimal strategy which is optimal for every discount factor close enough to one. In this article we explore this property, which we call as Blackwell–Nash equilibrium, in two player finite state-action discounted stochastic games. A strategy pair is said to
VIKAS VIKRAM SINGH +2 more
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Unternehmensforschung Operations Research - Recherche Opérationnelle, 1968
In this paper, we wish to introduce the idea of stochastic effects in optimal routing problems. This can be done by committing mistakes while making decisions. We will illustrate this idea by a classical puzzle: “The Wine Pouring Problem”.
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In this paper, we wish to introduce the idea of stochastic effects in optimal routing problems. This can be done by committing mistakes while making decisions. We will illustrate this idea by a classical puzzle: “The Wine Pouring Problem”.
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Stochastic Optimization of Regulators
Computers & Structures, 2017The optimal design of regulators is often based on the use of given, fixed nominal values of initial conditions, external loads and dynamic parameters of the control system. However, due to variations of material properties, tasks to be executed, modeling errors, etc., the model parameters are not exactly known and given quantities.
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