Generating moment matching scenarios using optimization techniques
Abstract
An optimization based method is proposed to generate moment matching scenarios for numerical integration and its use in stochastic programming. The main advantage of the method is its flexibility: it can generate scenarios matching any prescribed set of moments of the underlying distribution rather than matching all moments up to a certain order, and the distribution can be defined over an arbitrary set. This allows for a reduction in the number of scenarios and allows the scenarios to be better tailored to the problem at hand. The method is based on a semi-infinite linear programming formulation of the problem that is shown to be solvable with polynomial iteration complexity. A practical column generation method is implemented. The column generation subproblems are polynomial optimization problems; however, they need not be solved to optimality. It is found that the columns in the column generation approach can be efficiently generated by random sampling. The number of scenarios generated matches a lower bound of Tchakaloff's. The rate of convergence of the approximation error is established for continuous integrands, and an improved bound is given for smooth integrands. Extensive numerical experiments are presented in which variants of the proposed method are compared to Monte Carlomore »
- Authors:
-
- Northwestern Univ., Evanston, IL (United States)
- Publication Date:
- Research Org.:
- Northwestern Univ., Evanston, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- OSTI Identifier:
- 1321131
- Grant/Contract Number:
- SC0005102
- Resource Type:
- Accepted Manuscript
- Journal Name:
- SIAM Journal on Optimization
- Additional Journal Information:
- Journal Volume: 23; Journal Issue: 2; Journal ID: ISSN 1052-6234
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; scenario generation; moment matching; cubature; column generation; convex programming; statistical bounds; semi-infinite programming
Citation Formats
Mehrotra, Sanjay, and Papp, Dávid. Generating moment matching scenarios using optimization techniques. United States: N. p., 2013.
Web. doi:10.1137/110858082.
Mehrotra, Sanjay, & Papp, Dávid. Generating moment matching scenarios using optimization techniques. United States. https://doi.org/10.1137/110858082
Mehrotra, Sanjay, and Papp, Dávid. Thu .
"Generating moment matching scenarios using optimization techniques". United States. https://doi.org/10.1137/110858082. https://www.osti.gov/servlets/purl/1321131.
@article{osti_1321131,
title = {Generating moment matching scenarios using optimization techniques},
author = {Mehrotra, Sanjay and Papp, Dávid},
abstractNote = {An optimization based method is proposed to generate moment matching scenarios for numerical integration and its use in stochastic programming. The main advantage of the method is its flexibility: it can generate scenarios matching any prescribed set of moments of the underlying distribution rather than matching all moments up to a certain order, and the distribution can be defined over an arbitrary set. This allows for a reduction in the number of scenarios and allows the scenarios to be better tailored to the problem at hand. The method is based on a semi-infinite linear programming formulation of the problem that is shown to be solvable with polynomial iteration complexity. A practical column generation method is implemented. The column generation subproblems are polynomial optimization problems; however, they need not be solved to optimality. It is found that the columns in the column generation approach can be efficiently generated by random sampling. The number of scenarios generated matches a lower bound of Tchakaloff's. The rate of convergence of the approximation error is established for continuous integrands, and an improved bound is given for smooth integrands. Extensive numerical experiments are presented in which variants of the proposed method are compared to Monte Carlo and quasi-Monte Carlo methods on both numerical integration problems and stochastic optimization problems. The benefits of being able to match any prescribed set of moments, rather than all moments up to a certain order, is also demonstrated using optimization problems with 100-dimensional random vectors. Here, empirical results show that the proposed approach outperforms Monte Carlo and quasi-Monte Carlo based approaches on the tested problems.},
doi = {10.1137/110858082},
journal = {SIAM Journal on Optimization},
number = 2,
volume = 23,
place = {United States},
year = {Thu May 16 00:00:00 EDT 2013},
month = {Thu May 16 00:00:00 EDT 2013}
}
Web of Science
Works referenced in this record:
Risk aversion in multistage stochastic programming: A modeling and algorithmic perspective
journal, February 2016
- Homem-de-Mello, Tito; Pagnoncelli, Bernardo K.
- European Journal of Operational Research, Vol. 249, Issue 1
A Randomized Mirror-Prox Method for Solving Structured Large-Scale Matrix Saddle-Point Problems
journal, January 2013
- Baes, Michel; Bürgisser, Michael; Nemirovski, Arkadi
- SIAM Journal on Optimization, Vol. 23, Issue 2
Artificial neural network-based methodology for short-term electric load scenario generation
conference, September 2015
- Vagropoulos, Stylianos I.; Kardakos, Evaggelos G.; Simoglou, Christos K.
- 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP)
Monte Carlo sampling-based methods for stochastic optimization
journal, January 2014
- Homem-de-Mello, Tito; Bayraksan, Güzin
- Surveys in Operations Research and Management Science, Vol. 19, Issue 1
ANN-based scenario generation methodology for stochastic variables of electric power systems
journal, May 2016
- Vagropoulos, Stylianos I.; Kardakos, Evaggelos G.; Simoglou, Christos K.
- Electric Power Systems Research, Vol. 134
A Cutting Surface Algorithm for Semi-Infinite Convex Programming with an Application to Moment Robust Optimization
journal, January 2014
- Mehrotra, Sanjay; Papp, Dávid
- SIAM Journal on Optimization, Vol. 24, Issue 4
Containment Problems for Polytopes and Spectrahedra
journal, January 2013
- Kellner, Kai; Theobald, Thorsten; Trabandt, Christian
- SIAM Journal on Optimization, Vol. 23, Issue 2
An empirical analysis of scenario generation methods for stochastic optimization
journal, November 2016
- Löhndorf, Nils
- European Journal of Operational Research, Vol. 255, Issue 1
Scenario generation for stochastic optimization problems via the sparse grid method
journal, April 2015
- Chen, Michael; Mehrotra, Sanjay; Papp, Dávid
- Computational Optimization and Applications, Vol. 62, Issue 3
Multivariate simultaneous approximation
journal, December 2002
- Bagby, T.; Bos, L.; Levenberg, N.
- Constructive Approximation, Vol. 18, Issue 4
An encyclopaedia of cubature formulas
journal, June 2003
- Cools, Ronald
- Journal of Complexity, Vol. 19, Issue 3
A new moment matching algorithm for sampling from partially specified symmetric distributions
journal, November 2008
- Date, P.; Mamon, R.; Jalen, L.
- Operations Research Letters, Vol. 36, Issue 6
The complexity of optimizing over a simplex, hypercube or sphere: a short survey
journal, December 2007
- de Klerk, Etienne
- Central European Journal of Operations Research, Vol. 16, Issue 2
Scenario reduction in stochastic programming
journal, March 2003
- Dupa?ov�, J.; Gr�we-Kuska, N.; R�misch, W.
- Mathematical Programming, Vol. 95, Issue 3
Simulation and optimization approaches to scenario tree generation
journal, April 2004
- Gülpınar, Nalan; Rustem, Berç; Settergren, Reuben
- Journal of Economic Dynamics and Control, Vol. 28, Issue 7
Likelihood approximation by numerical integration on sparse grids
journal, May 2008
- Heiss, Florian; Winschel, Viktor
- Journal of Econometrics, Vol. 144, Issue 1
Scenario tree modeling for multistage stochastic programs
journal, November 2007
- Heitsch, Holger; Römisch, Werner
- Mathematical Programming, Vol. 118, Issue 2
GloptiPoly: Global optimization over polynomials with Matlab and SeDuMi
journal, June 2003
- Henrion, Didier; Lasserre, Jean-Bernard
- ACM Transactions on Mathematical Software, Vol. 29, Issue 2
Component-by-component constructions achieve the optimal rate of convergence for multivariate integration in weighted Korobov and Sobolev spaces
journal, June 2003
- Kuo, F. Y.
- Journal of Complexity, Vol. 19, Issue 3
Global Optimization with Polynomials and the Problem of Moments
journal, January 2001
- Lasserre, Jean B.
- SIAM Journal on Optimization, Vol. 11, Issue 3
An Approximate Method for Sampling Correlated Random Variables from Partially-Specified Distributions
journal, February 1998
- Lurie, Philip M.; Goldberg, Matthew S.
- Management Science, Vol. 44, Issue 2
Epi-convergent discretizations of stochastic programs via integration quadratures
journal, February 2005
- Pennanen, Teemu; Koivu, Matti
- Numerische Mathematik, Vol. 100, Issue 1
Discrepancy and integration of continuous functions
journal, February 1988
- Proinov, Petko D.
- Journal of Approximation Theory, Vol. 52, Issue 2
Scenario Generation
book, February 2011
- Römisch, Werner
- Wiley Encyclopedia of Operations Research and Management Science
A comparison between (quasi-)Monte Carlo and cubature rule based methods for solving high-dimensional integration problems
journal, March 2003
- Schürer, Rudolf
- Mathematics and Computers in Simulation, Vol. 62, Issue 3-6
Sums of Squares and Semidefinite Program Relaxations for Polynomial Optimization Problems with Structured Sparsity
journal, January 2006
- Waki, Hayato; Kim, Sunyoung; Kojima, Masakazu
- SIAM Journal on Optimization, Vol. 17, Issue 1
Approximating the complexity measure of Vavasis-Ye algorithm is NP-hard
journal, September 1999
- Tunçel, Levent
- Mathematical Programming, Vol. 86, Issue 1
Works referencing / citing this record:
A copula-based scenario tree generation algorithm for multiperiod portfolio selection problems
journal, January 2019
- Yan, Zhe; Chen, Zhiping; Consigli, Giorgio
- Annals of Operations Research, Vol. 292, Issue 2
Scenario generation for stochastic optimization problems via the sparse grid method
journal, April 2015
- Chen, Michael; Mehrotra, Sanjay; Papp, Dávid
- Computational Optimization and Applications, Vol. 62, Issue 3
An empirical analysis of scenario generation methods for stochastic optimization
journal, November 2016
- Löhndorf, Nils
- European Journal of Operational Research, Vol. 255, Issue 1
Scenario generation in stochastic programming using principal component analysis based on moment-matching approach
journal, October 2019
- Chopra, Isha; Selvamuthu, Dharmaraja
- OPSEARCH, Vol. 57, Issue 1
Performance Comparison of Scenario-Generation Methods Applied to a Stochastic Optimization Asset-Liability Management Model
journal, April 2018
- Oliveira, Alan Delgado de; Filomena, Tiago Pascoal; Righi, Marcelo Brutti
- Pesquisa Operacional, Vol. 38, Issue 1
ANN-based scenario generation methodology for stochastic variables of electric power systems
journal, May 2016
- Vagropoulos, Stylianos I.; Kardakos, Evaggelos G.; Simoglou, Christos K.
- Electric Power Systems Research, Vol. 134
Risk aversion in multistage stochastic programming: A modeling and algorithmic perspective
journal, February 2016
- Homem-de-Mello, Tito; Pagnoncelli, Bernardo K.
- European Journal of Operational Research, Vol. 249, Issue 1