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Page 7702 of Mathematical Reviews Vol. , Issue 2002J [page]

<span title="">2002</span> <i title="American Mathematical Society"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_mathematical-reviews" style="color: black;">Mathematical Reviews </a> </i> &nbsp;
Summary: “Methods for solving stochastic programming (SP) problems by a finite series of Monte Carlo samples are considered.  ...  Tigan {“On a method for fractional optimization problems.  ... 
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Analysis and Control of Stochastic Systems using Semidefinite Programming over Moments [article]

Andrew Lamperski and Khem Raj Ghusinga and Abhyudai Singh
<span title="2017-02-01">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Furthermore, we show how an approximate optimal control strategy can be constructed from the solution of the semidefinite program. The results are illustrated using numerous examples.  ...  Existing methods for stochastic analysis, known as closure methods, focus on approximating this infinite system of equations with a finite dimensional system.  ...  CONCLUSION This paper presented a method based on semidefinite programming for computing bounds on stochastic process moments and stochastic optimal control problems in a unified manner.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1702.00422v1">arXiv:1702.00422v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xpf7al4tjzbdrbulqf3trx3ndy">fatcat:xpf7al4tjzbdrbulqf3trx3ndy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200827145606/https://arxiv.org/pdf/1702.00422v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/be/93/be93b2cc5aba9a9b53d2c52c34c4c6ad025e2712.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1702.00422v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Page 4361 of Mathematical Reviews Vol. , Issue 99f [page]

<span title="">1999</span> <i title="American Mathematical Society"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_mathematical-reviews" style="color: black;">Mathematical Reviews </a> </i> &nbsp;
Summary: “Monte Carlo sampling-based algorithms hold much promise for solving stochastic programs with many scenarios.  ...  (English summary) Semidefinite programming and interior-point approaches for combinatorial optimization problems (Toronto, ON, 1996). J. Comb. Optim. 2 (1998), no. 1, 29-50.  ... 
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Approximate Dynamic Programming via Sum of Squares Programming [article]

Tyler H. Summers, Konstantin Kunz, Nikolaos Kariotoglou, Maryam Kamgarpour, Sean Summers, John Lygeros
<span title="2012-12-06">2012</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
An approximate value function can then be computed offline by solving a semidefinite program, without having to sample the infinite constraint.  ...  We describe an approximate dynamic programming method for stochastic control problems on infinite state and input spaces.  ...  Also, the methods can be applied in stochastic reachability problems, which is explored in a companion paper [25] via radial basis functions and constraint sampling techniques.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1212.1269v1">arXiv:1212.1269v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3pd5gcd5k5dmdfb4anhupaacne">fatcat:3pd5gcd5k5dmdfb4anhupaacne</a> </span>
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Stochastic Second-Order Cone Programming in Mobile Ad Hoc Networks

F. Maggioni, F. A. Potra, M. I. Bertocchi, E. Allevi
<span title="2009-05-14">2009</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ynhjdrvpsnbidmoniqokzz5ota" style="color: black;">Journal of Optimization Theory and Applications</a> </i> &nbsp;
We propose a two-stage stochastic second-order cone programming formulation of the semidefinite stochastic location-aided routing (SLAR) model, described in Ariyawansa and Zhu (Q. J. Oper.  ...  By using a second-order cone model, we are able to solve problems with a much larger number of scenarios (20250) than it is possible with the semidefinite model (500).  ...  However, if an interior point method is used for solving the resulting optimization problem, then at each iteration of the algorithm the matrix in (40) is positive definite so that I(z) = {1, . . . , n  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10957-009-9561-0">doi:10.1007/s10957-009-9561-0</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/l6xogewmlzhzxoi6rukzuupioi">fatcat:l6xogewmlzhzxoi6rukzuupioi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170505062408/http://www.math.umbc.edu:80/~potra/MPBA09mobile.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1a/41/1a41dddcd4165ff99f3e83a38939340f4565b2c3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10957-009-9561-0"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Convex approximations in stochastic programming by semidefinite programming

István Deák, Imre Pólik, András Prékopa, Tamás Terlaky
<span title="2011-10-01">2011</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kov3rwzipzf2dmxyohqwfpxrsm" style="color: black;">Annals of Operations Research</a> </i> &nbsp;
Using several approaches for constructing convex approximations we present some optimization models yielding convex quadratic regressions that are optimal approximations in L 1 , L∞ and L 2 norm.  ...  The following question arises in stochastic programming: how can one approximate a noisy convex function with a convex quadratic function that is optimal in some sense.  ...  Prékopa pointed out that the approximation used in these numerical procedures can be made convex by semidefinite optimization.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10479-011-0986-0">doi:10.1007/s10479-011-0986-0</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jo4ndihl35ewxdl22w6nud47be">fatcat:jo4ndihl35ewxdl22w6nud47be</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190217183737/http://pdfs.semanticscholar.org/0689/d683696dadf279abb1b21d34bc64c2a8fa03.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/06/89/0689d683696dadf279abb1b21d34bc64c2a8fa03.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10479-011-0986-0"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Page 8828 of Mathematical Reviews Vol. , Issue 2003k [page]

<span title="">2003</span> <i title="American Mathematical Society"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_mathematical-reviews" style="color: black;">Mathematical Reviews </a> </i> &nbsp;
(English summary) Stochastic programming. Optim. Methods Softw. 17 (2002), no. 3, 383-407.  ...  (English summary) Stochastic programming. Optim. Methods Softw. 17 (2002), no. 3, 445-492.  ... 
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Semidefinite Relaxations for Stochastic Optimal Control Policies [article]

Matanya B. Horowitz, Joel W. Burdick
<span title="2014-02-12">2014</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This work proposes a new method obtaining approximate solutions to these linear stochastic optimal control (SOC) problems.  ...  A Sum of Squares (SOS) relaxation is then taken to the partial differential constraints, leading to a hierarchy of semidefinite relaxations with improving sub-optimality gap.  ...  This paper presents a novel alternative method to solve such problems using polynomial optimization and semidefinite programming.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1402.2763v1">arXiv:1402.2763v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mdq5v63kfjdgrnhmefvdw6sj5m">fatcat:mdq5v63kfjdgrnhmefvdw6sj5m</a> </span>
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Inexact SA Method for Constrained Stochastic Convex SDP and Application in Chinese Stock Market

Shuang Chen, Li-Ping Pang, Jian Lv, Zun-Quan Xia
<span title="">2018</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rvoz2f4y2jehxeukagl2wrnina" style="color: black;">Journal of Function Spaces</a> </i> &nbsp;
We propose stochastic convex semidefinite programs (SCSDPs) to handle uncertain data in applications.  ...  For these models, we design an efficient inexact stochastic approximation (SA) method and prove the convergence, complexity, and robust treatment of the algorithm.  ...  Introduction In this paper, we propose a class of optimization problems called stochastic convex semidefinite programs (SCSDPs): min ( ) = E [ ( , )] s.t.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2018/3742575">doi:10.1155/2018/3742575</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/z7rha6zcirh3teblvkitiowece">fatcat:z7rha6zcirh3teblvkitiowece</a> </span>
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Stochastic AC optimal power flow with affine recourse

Raphael Louca, Eilyan Bitar
<span title="">2016</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wjd7b2sxyfahnaei4xvi46vwsu" style="color: black;">2016 IEEE 55th Conference on Decision and Control (CDC)</a> </i> &nbsp;
With the increasing penetration of intermittent renewable energy sources into the electric power grid, there is an emerging need to develop stochastic optimization methods to enable the reliable and efficient  ...  This problem amounts to an infinitedimensional nonconvex optimization problem. We develop a finite-dimensional inner approximation as a semidefinite program.  ...  We provide a method to approximate the stochastic AC OPF problem from within by a semidefinite program.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cdc.2016.7798626">doi:10.1109/cdc.2016.7798626</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cdc/LoucaB16.html">dblp:conf/cdc/LoucaB16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yh2j2whalnad3ehuud6ss3se2a">fatcat:yh2j2whalnad3ehuud6ss3se2a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170612162829/http://foie.ece.cornell.edu/%7Elouca/Louca_CDC16_OPF.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2d/59/2d595b8075d87e78532e6e38905f363845c29f6d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cdc.2016.7798626"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

A dual stochastic DFP algorithm for optimal resource allocation in wireless systems

Aryan Mokhtari, Alejandro Ribeiro
<span title="">2013</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yyhqnzanmjgq3gg3m5e5vh2noy" style="color: black;">2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC)</a> </i> &nbsp;
A stochastic implementation of the Davidon-Fletcher-Powell (DFP) quasi-Newton method to minimize dual functions of optimal resource allocation problems in wireless systems is introduced.  ...  While the use of dual stochastic gradient descent algorithms is widespread, they suffer from slow convergence rate.  ...  channel samples {ht} ∞ t=1 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/spawc.2013.6612004">doi:10.1109/spawc.2013.6612004</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/spawc/MokhtariR13.html">dblp:conf/spawc/MokhtariR13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nra6yc5rdvcq5ikb7ewo3le5l4">fatcat:nra6yc5rdvcq5ikb7ewo3le5l4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20151203190130/http://www.seas.upenn.edu/~aribeiro/preprints/c_2013_mokhtari_ribeiro.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/37/12/37124ff1bda58810819d72815bae5a1cea728472.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/spawc.2013.6612004"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

A STOCHASTIC APPROXIMATION ALGORITHM FOR STOCHASTIC SEMIDEFINITE PROGRAMMING

Bruno Gaujal, Panayotis Mertikopoulos
<span title="2016-05-18">2016</span> <i title="Cambridge University Press (CUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rpneo5vrqnhrvdhqx2rs5s2y4m" style="color: black;">Probability in the engineering and informational sciences (Print)</a> </i> &nbsp;
Motivated by applications to multi-antenna wireless networks, we propose a distributed and asynchronous algorithm for stochastic semidefinite programming.  ...  We show that the resulting algorithm converges almost surely to an ɛ-approximation of the optimal solution requiring only an unbiased estimate of the gradient of the problem's stochastic objective.  ...  ; as a result, route discovery in mobile ad-hoc networks is typically addressed using stochastic semidefinite programming approaches [43] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1017/s0269964816000127">doi:10.1017/s0269964816000127</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zlmyyhtbgvbf3oxjlcyu4bd7mu">fatcat:zlmyyhtbgvbf3oxjlcyu4bd7mu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20181030060043/https://www.cambridge.org/core/services/aop-cambridge-core/content/view/C4888BCA21C1C9CC6A4B8DA2BD405F20/S0269964816000127a.pdf/div-class-title-a-stochastic-approximation-algorithm-for-stochastic-semidefinite-programming-div.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d3/9b/d39bb33030b73de7359d9cdcb1a11ef4dd006703.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1017/s0269964816000127"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> cambridge.org </button> </a>

Efficient Semidefinite Spectral Clustering via Lagrange Duality [article]

Yan Yan, Chunhua Shen, Hanzi Wang
<span title="2014-02-22">2014</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose an efficient approach to semidefinite spectral clustering (SSC), which addresses the Frobenius normalization with the positive semidefinite (p.s.d.) constraint for spectral clustering.  ...  In this paper, SSC is formulated as a semidefinite programming (SDP) problem.  ...  Computational Complexity The optimization problem of SSC is a semidefinite programming (SDP) problem, which allows us to use off-the-shelf SDP solvers.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1402.5497v1">arXiv:1402.5497v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aum3ieoi7rbvbkq3rbowpwz6xq">fatcat:aum3ieoi7rbvbkq3rbowpwz6xq</a> </span>
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Page 8694 of Mathematical Reviews Vol. , Issue 2002K [page]

<span title="">2002</span> <i title="American Mathematical Society"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_mathematical-reviews" style="color: black;">Mathematical Reviews </a> </i> &nbsp;
Then we show that in or- der to solve these max-closed IP problems, simplicial path follow- ing methods can be used.  ...  A method is proposed for the estimation of the latter from the results of a local steepest as- cent from a sample of random starting points.  ... 
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Page 1481 of Mathematical Reviews Vol. , Issue 2003B [page]

<span title="">2003</span> <i title="American Mathematical Society"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_mathematical-reviews" style="color: black;">Mathematical Reviews </a> </i> &nbsp;
(English summary) Optim. Methods Softw. 15 (2001), no. 1, 1-28. In this paper the authors investigate the use of the Gauss-Newton (GN) direction in semidefinite programming (SDP).  ...  This type of optimization problem covers stochastic programming problems with penalty and recourse.  ... 
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