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Stochastic programming

About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.


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BookDOI
01 Jan 1997
TL;DR: In this article, the authors considered the problem of finding the optimal bound on the average of a Rounded-off Observation in the presence of a single moment condition and showed that it is NP-hard.
Abstract: Preface. Optimal Bounds on the Average of a Rounded-off Observation in the Presence of a Single Moment Condition G.A. Anastassio. The Complete Solution of a Rounding Problem Under Two Moment Conditions T. Rychlik. Methods of Realization of Moment Problems with Entropy Maximization V. Girardin. Matrices of Higher Moments: Some Problems of Representation E. Kaarik. The Method of Moments in Tomography and Quantum Mechanics L.B. Klebanov, S.T. Rachev. Moment Problems in Stochastic Geometry V. Benes. Frechet Classes and Nonmonotone Dependence M. Scarsini, M. Shaked. Comonotonicity, Rank-Dependent Utilities and a Search Problem A. Chateauneuf, et al. A Stochastic Ordering Based on a Decomposition of Kendall's Tau P. Caperaa, et al. Maximum Entropy Distributions with Prescribed Marginals and Normal Score Correlations M.J.W. Jansen. On Bivariate Distributions with Polya-Aeppli or Luders-Delaporte Marginals V.E. Piperigou. Boundary Distributions with Fixed Marginals E.-M. Tiit, H.-L. Helemae. On Approximations of Copulas X. Li, et al. Joint Distributions of Two Uniform Random Variables When the Sum and Difference are Independent G. Dall'Aglio. Diagonal Copulas R.B. Nelsen, G.A. Fredricks. Copulas Constructed from Diagonal Sections G.A. Fredricks, R.B. Nelsen. Continuous Scaling on a Bivariate Copula C.M. Cuadras, J. Fortiana. Representation of Markov Kernels by Random Mappings Under Order Conditions H.G. Kellerer. How to Construct a Two- Dimensional Random Vector with a Given Conditional Structure J. Stepan. Strassen's Theorem for Group-Valued Charges A. Hirshberg, R.M. Shortt. The Lancaster's Probabilities on R2 and Their Extreme Points G. Letac. On Marginalization, Collapsibility andPrecollapsibility M. Studeny. Moment Bounds for Stochastic Programs in Particular for Recourse Problems J. Dupacova. Probabilistic Constrained Programming and Distributions with Given Marginals T. Szantai. On an e-solution of Minimax Problem in Stochastic Programming V. Kankova. Bounds for Stochastic Programs -- Nonconvex Case T. Visek. Artificial Intelligence, the Marginal Problem and Inconsistency R. Jirousek. Inconsistent Marginal Problem on Finite Sets O. Kriz. Topics in the Duality for Mass Transfer Problems V.L. Levin. Generalising Monotonicity C.S. Smith, M. Knott. On Optimal Multivariate Couplings L. Ruschendorf, L. Uckelmann. Optimal Couplings Between One-Dimensional Distributions L. Uckelmann. Duality Theorems for Assignments with Upper Bounds D. Ramachandran, L. Ruschendorf. Bounding the Moments of an Order Statistics if Each k-Tuple is Independent J.H.B. Kemperman. Subject Index.

150 citations

Proceedings ArticleDOI
09 Oct 2011
TL;DR: The experimental results show that stochastic implementations tolerate more noise and consume less hardware than their conventional counterparts, and the validity of the present stoChastic computational elements is demonstrated through four basic digital image processing algorithms.
Abstract: As device scaling continues to nanoscale dimensions, circuit reliability will continue to become an ever greater problem. Stochastic computing, which performs computing with random bits (stochastic bits streams), can be used to enable reliable computation using those unreliable devices. However, one of the major issues of stochastic computing is that applications implemented with this technique are limited by the available computational elements. In this paper, first we will introduce and prove a stochastic absolute value function. Second, we will demonstrate a mathematical analysis of a stochastic tanh function, which is a key component used in a stochastic comparator. Third, we will present a quantitative analysis of a one-parameter linear gain function, and propose a new two-parameter version. The validity of the present stochastic computational elements is demonstrated through four basic digital image processing algorithms: edge detection, frame difference based image segmentation, median filter based noise reduction, and image contrast stretching. Our experimental results show that stochastic implementations tolerate more noise and consume less hardware than their conventional counterparts.

150 citations

Journal ArticleDOI
TL;DR: This paper studies distributionally robust stochastic programming in a setting where there is a specified reference probability measure and the uncertainty set of probability measures consists of measures in some sense close to the reference measure.
Abstract: In this paper we study distributionally robust stochastic programming in a setting where there is a specified reference probability measure and the uncertainty set of probability measures consists of measures in some sense close to the reference measure. We discuss law invariance of the associated worst case functional and consider two basic constructions of such uncertainty sets. Finally we illustrate some implications of the property of law invariance.

150 citations

Journal ArticleDOI
TL;DR: This work revisits the sparse linear algebra computations of the parallel solver PIPS with the goal of improving the shared-memory performance and decreasing the time to solution.
Abstract: We present a scalable approach and implementation for solving stochastic optimization problems on high-performance computers. In this work we revisit the sparse linear algebra computations of the parallel solver PIPS with the goal of improving the shared-memory performance and decreasing the time to solution. These computations consist of solving sparse linear systems with multiple sparse right-hand sides and are needed in our Schur-complement decomposition approach to compute the contribution of each scenario to the Schur matrix. Our novel approach uses an incomplete augmented factorization implemented within the PARDISO linear solver and an outer BiCGStab iteration to efficiently absorb pivot perturbations occurring during factorization. This approach is capable of both efficiently using the cores inside a computational node and exploiting sparsity of the right-hand sides. We report on the performance of the approach on high-performance computers when solving stochastic unit commitment problems of unpre...

150 citations

Journal ArticleDOI
TL;DR: In this paper, the optimal involvement in a futures electricity market of a power producer to hedge against the risk of pool price volatility is addressed, where a stochastic programming framework with recourse is used to model this decision-making problem.
Abstract: This paper addresses the optimal involvement in a futures electricity market of a power producer to hedge against the risk of pool price volatility. The considered trading horizon spans one whole year. Recognizing the highly uncertain nature of future pool prices, a stochastic programming framework with recourse is used to model this decision-making problem. The resulting problem is a large scale mixed-integer linear programming problem. Scenario reduction techniques are used to make this problem tractable. Risk is properly modeled using the CVaR methodology. Results from a realistic case study are provided and analyzed. Some conclusions are finally drawn.

149 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532