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Author Espinoza, D.; Moreno, E. pdf  doi
openurl 
  Title A primal-dual aggregation algorithm for minimizing conditional value-at-risk in linear programs Type
  Year (up) 2014 Publication Computational Optimization And Applications Abbreviated Journal Comput. Optim. Appl.  
  Volume 59 Issue 3 Pages 617-638  
  Keywords Conditional value at risk; Aggregation techniques; Approximation methods; Sample average approximation  
  Abstract Recent years have seen growing interest in coherent risk measures, especially in Conditional Value-at-Risk (). Since is a convex function, it is suitable as an objective for optimization problems when we desire to minimize risk. In the case that the underlying distribution has discrete support, this problem can be formulated as a linear programming (LP) problem. Over more general distributions, recent techniques, such as the sample average approximation method, allow to approximate the solution by solving a series of sampled problems, although the latter approach may require a large number of samples when the risk measures concentrate on the tail of the underlying distributions. In this paper we propose an automatic primal-dual aggregation scheme to exactly solve these special structured LPs with a very large number of scenarios. The algorithm aggregates scenarios and constraints in order to solve a smaller problem, which is automatically disaggregated using the information of its dual variables. We compare this algorithm with other common approaches found in related literature, such as an improved formulation of the full problem, cut-generation schemes and other problem-specific approaches available in commercial software. Extensive computational experiments are performed on portfolio and general LP instances.  
  Address [Espinoza, Daniel] Univ Chile, Dept Ind Engn, Santiago, Chile, Email: daespino@dii.uchile.cl;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0926-6003 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000344803000009 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 424  
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Author Munoz, G.; Espinoza, D.; Goycoolea, M.; Moreno, E.; Queyranne, M.; Rivera Letelier, O. pdf  doi
openurl 
  Title A study of the Bienstock-Zuckerberg algorithm: applications in mining and resource constrained project scheduling Type
  Year (up) 2018 Publication Computational Optimization And Applications Abbreviated Journal Comput. Optim. Appl.  
  Volume 69 Issue 2 Pages 501-534  
  Keywords Column generation; Dantzig-Wolfe; Optimization; RCPSP  
  Abstract We study a Lagrangian decomposition algorithm recently proposed by Dan Bienstock and Mark Zuckerberg for solving the LP relaxation of a class of open pit mine project scheduling problems. In this study we show that the Bienstock-Zuckerberg (BZ) algorithm can be used to solve LP relaxations corresponding to a much broader class of scheduling problems, including the well-known Resource Constrained Project Scheduling Problem (RCPSP), and multi-modal variants of the RCPSP that consider batch processing of jobs. We present a new, intuitive proof of correctness for the BZ algorithm that works by casting the BZ algorithm as a column generation algorithm. This analysis allows us to draw parallels with the well-known Dantzig-Wolfe decomposition (DW) algorithm. We discuss practical computational techniques for speeding up the performance of the BZ and DW algorithms on project scheduling problems. Finally, we present computational experiments independently testing the effectiveness of the BZ and DW algorithms on different sets of publicly available test instances. Our computational experiments confirm that the BZ algorithm significantly outperforms the DW algorithm for the problems considered. Our computational experiments also show that the proposed speed-up techniques can have a significant impact on the solve time. We provide some insights on what might be explaining this significant difference in performance.  
  Address [Munoz, Gonzalo] Columbia Univ, Ind Engn & Operat Res, New York, NY USA, Email: marcos.goycoolea@uai.cl  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0926-6003 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000426295000009 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 835  
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Author Canessa, G.; Gallego, J.A.; Ntaimo, L.; Pagnoncelli, B.K. pdf  doi
openurl 
  Title An algorithm for binary linear chance-constrained problems using IIS Type
  Year (up) 2019 Publication Computational Optimization And Applications Abbreviated Journal Comput. Optim. Appl.  
  Volume 72 Issue 3 Pages 589-608  
  Keywords Chance-constrained programming; Infeasible irreducible subsystems; Integer programming  
  Abstract We propose an algorithm based on infeasible irreducible subsystems to solve binary linear chance-constrained problems with random technology matrix. By leveraging on the problem structure we are able to generate good quality upper bounds to the optimal value early in the algorithm, and the discrete domain is used to guide us efficiently in the search of solutions. We apply our methodology to individual and joint binary linear chance-constrained problems, demonstrating the ability of our approach to solve those problems. Extensive numerical experiments show that, in some cases, the number of nodes explored by our algorithm is drastically reduced when compared to a commercial solver.  
  Address [Canessa, Gianpiero] Univ Adolfo Ibanez, Diagonal Torres 2640, Santiago 7941169, Chile, Email: gianpiero.canessa@edu.uai.cl  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0926-6003 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000463792400003 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 996  
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