Results 11 to 20 of about 72,594 (306)
Approximation in stochastic integer programming
, 2003 Approximation algorithms are the prevalent solution methods in the field of stochastic programming. Problems in this field are very hard to solve. Indeed, most of the research in this field has concentrated on designing solution methods that approximate the optimal solutions.
Stougie, Leen, Vlerk, Maarten H. van der
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Efficiency of the stochastic approximation method [PDF]
Yugoslav Journal of Operations Research, 2012 The practical aspect of the stochastic approximation method (SA) is studied. Specifically, we investigated the efficiency depending on the coefficients that generate the step length in optimization algorithm, as well as the efficiency depending on the
Japundžić Miloš
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A Continuous Dynamic Stochastic Approximation Procedure [PDF]
The Egyptian Statistical Journal, 1993 This paper considers the continuous Kiefer-Nolfowitz stochastic approximation procedure, where the regression function changes with time t. Let ED(t) be the unique minimum (maximum) of the regression function at a time t.
El Sayed Sorour
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Pathwise Convergent Approximation for the Fractional SDEs
Mathematics, 2022 Fractional stochastic differential equation (FSDE)-based random processes are used in a wide spectrum of scientific disciplines. However, in the majority of cases, explicit solutions for these FSDEs do not exist and approximation schemes have to be ...
Kęstutis Kubilius, Aidas Medžiūnas
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Stochastic approximation versus sample average approximation for Wasserstein barycenters
, 2021 In the machine learning and optimization community, there are two main approaches for the convex risk minimization problem, namely the Stochastic Approximation (SA) and the Sample Average Approximation (SAA).
Dvinskikh, Darina
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Stochastic Entropy Solutions for Stochastic Scalar Balance Laws
Entropy, 2019 We are concerned with the initial value problem for a multidimensional balance law with multiplicative stochastic perturbations of Brownian type.
Jinlong Wei +3 more
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Best Response Computation in Multiplayer Imperfect-Information Stochastic Games
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Computing a best response is a fundamental task in game theory. One of its uses is to compute the degree of approximation error of an approximation of Nash equilibrium strategies.
Sam Ganzfried
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Stochastic Approximations and Differential Inclusions [PDF]
SIAM Journal on Control and Optimization, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Michel Benaïm +2 more
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Asymptotic Analysis for One-Stage Stochastic Linear Complementarity Problems and Applications
Mathematics, 2023 One-stage stochastic linear complementarity problem (SLCP) is a special case of a multi-stage stochastic linear complementarity problem, which has important applications in economic engineering and operations management.
Shuang Lin, Jie Zhang, Chen Qiu
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Submodular stochastic probing on matroids [PDF]
, 2014 In a stochastic probing problem we are given a universe E, where each element e in E is active independently with probability p in [0,1], and only a probe of e can tell us whether it is active or not. On this universe we execute a process that one by one
Sviridenko, Maxim +5 more
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