On downloading and using COIN-OR for solving linear/integer optimization problems

A more recent version of this paper has been published as Working Paper Biltoki 2011.08.COIN-OR, source code, optimization software

On Downloading and Using CPLEX within COIN-OR for Solving Linear/Integer Optimization Problems

The aim of this technical report is to present some detailed explanations in order to use the solver CPLEX within COIN-OR environment. In particular, we describe how to download, install and use the corresponding source code and libraries under Windows and Linux operating systems. We will use an example taken from the literature, with the experimental code and files written in C++, to describe the whole process of editing, compiling and running the executable, to solve this optimization problem by using this software. In the case of the Windows environment, a C++ compiler is also needed. We will use the Visual C++ 2010 Express Edition.CPLEX, COIN-OR, C++

On downloading and using COIN-OR for solving linear/integer optimization problems

A more recent version of this paper has been published as Working Paper Biltoki 2011.08.The aim of this technical report is to present some detailed explanations in order to help to use the open source software for optimization COIN-OR. In particular, we describe how to download, install and use the corresponding solvers under Windows and Linux operating systems. We will use an example taken from the literature, with the corresponding source code written in C++, to describe the whole process of editing, compiling and running the executable, to solve this optimization problem by using this software.This research has been partially supported by the projects ECO2008-00777 ECON from the Ministry of Education and Science, and Grupo de Investigación IT-347-10 from the Basque Government, Spain

On Downloading and Using CPLEX within COIN-OR for Solving Linear/Integer Optimization Problems

The aim of this technical report is to present some detailed explanations in order to use the solver CPLEX within COIN-OR environment. In particular, we describe how to download, install and use the corresponding source code and libraries under Windows and Linux operating systems. We will use an example taken from the literature, with the experimental code and files written in C++, to describe the whole process of editing, compiling and running the executable, to solve this optimization problem by using this software. In the case of the Windows environment, a C++ compiler is also needed. We will use the Visual C++ 2010 Express Edition

A parallelizable algorithmic framework for solving large scale multi-stage stochastic mixed 0-1 problems under uncertainty

Preprint submitted to Computers & Operations Researchmulti-stage stochastic mixed 0-1 optimization, nonsymmetric scenario trees, implicit and explicit nonanticipativity constraints, splitting variable and compact representations, scenario cluster partitioning

Lagrangean decomposition for large-scale two-stage stochastic mixed 0-1 problems

In this paper we study solution methods for solving the dual problem corresponding to the Lagrangean Decomposition of two stage stochastic mixed 0-1 models. We represent the two stage stochastic mixed 0-1 problem by a splitting variable representation of the deterministic equivalent model, where 0-1 and continuous variables appear at any stage. Lagrangean Decomposition is proposed for satisfying both the integrality constraints for the 0-1 variables and the non-anticipativity constraints. We compare the performance of four iterative algorithms based on dual Lagrangean Decomposition schemes, as the Subgradient method, the Volume algorithm, the Progressive Hedging algorithm and the Dynamic Constrained Cutting Plane scheme. We test the conditions and properties of convergence for medium and large-scale dimension stochastic problems. Computational results are reported.Progressive Hedging algorithm, volume algorithm, Lagrangean decomposition, subgradient method

A note on the implementation of the BFC-MSMIP algorithm in C++ by using COIN-OR as an optimization engine

The aim of this technical report is to present some detailed explanations in order to help to understand and use the algorithm Branch and Fix Coordination for solving MultiStage Mixed Integer Problems (BFC- MSMIP). We have developed an algorithmic approach implemented in a C++ experimental code that uses the optimization engine COmputational INfrastructure for Operations Research (COIN-OR) for solving the auxiliary linear and mixed 0-1 submodels. Now, we give the computational and implementational descrip- tion in order to use this open optimization software not only in the implementation of our procedure but also in similar schemes to be developed by the users.nonanticipativity constraints, cluster partitioning, COIN-OR library, branch-and-fix coordination, multi-stage stochastic mixed 0-1 programming

Generating cluster submodels from a multistage stochastic mixed integer optimization model using break stage

We present a scheme to generate clusters submodels with stage ordering from a (symmetric or a nonsymmetric one) multistage stochastic mixed integer optimization model using break stage. We consider a stochastic model in compact representation and MPS format with a known scenario tree. The cluster submodels are built by storing first the 0-1 the variables, stage by stage, and then the continuous ones, also stage by stage. A C++ experimental code has been implemented for reordering the stochastic model as well as the cluster decomposition after the relaxation of the non-anticipativiy constraints until the so-called breakstage. The computational experience shows better performance of the stage ordering in terms of elapsed time in a randomly generated testbed of multistage stochastic mixed integer problems.This research has been partially supported by the projects MTM2012-31514 from the Spanish Ministry of Economy and Competitiveness, Grupo de Investigación IT-567-13 of the Basque Government, UFI BETS 2011 of the University of Basque Country (UPV/EHU), Spain, and Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo (CYTED 2011). The computational resources were provided by SGI/IZO-SGIker a t UPV/EHU (supported by the Spanish Ministry of Education and Science and the European Social Fund)

A parallelizable algorithmic framework for solving large scale multi-stage stochastic mixed 0-1 problems under uncertainty

Preprint submitted to Computers & Operations ResearchIn this paper we present a parallelizable scheme of the Branch-and-Fix Coordination algorithm for solving medium and large scale multi-stage mixed 0-1 optimization problems under uncertainty. The uncertainty is represented via a nonsymmetric scenario tree. An information structuring for scenario cluster partitioning of nonsymmetric scenario trees is also presented, given the general model formulation of a multi-stage stochastic mixed 0-1 problem. The basic idea consists of explicitly rewriting the nonanticipativity constraints (NAC) of the 0-1 and continuous variables in the stages with common information. As a result an assignment of the constraint matrix blocks into independent scenario cluster submodels is performed by a so-called cluster splitting-compact representation. This partitioning allows to generate a new information structure to express the NAC which link the related clusters, such that the explicit NAC linking the submodels together is performed by a splitting variable representation. The new algorithm has been implemented in a C++ experimental code that uses the open source optimization engine COIN-OR, for solving the auxiliary linear and mixed 0-1 submodels. Some computational experience is reported to validate the new proposed approach. We give computational evidence of the model tightening effect that have preprocessing techniques in stochastic integer optimization as well, by using the probing and Gomory and clique cuts identification and appending schemes of the optimization engine.This research has been partially supported by the projects ECO2008-00777 ECON from the Ministry of Education and Science, Grupo de Investigación IT-347-10 from the Basque Government, URJC-CM-2008-CET-3703 and RIESGOS CM from Comunidad de Madrid, and PLANIN MTM2009-14087-C04-01 from Ministry of Science and Innovation, Spain

Scenario Cluster Lagrangian Decomposition in two stage stochastic mixed 0-1 optimization

In this paper we introduce four scenario Cluster based Lagrangian Decomposition (CLD) procedures for obtaining strong lower bounds to the (optimal) solution value of two-stage stochastic mixed 0-1 problems. At each iteration of the Lagrangian based procedures, the traditional aim consists of obtaining the solution value of the corresponding Lagrangian dual via solving scenario submodels once the nonanticipativity constraints have been dualized. Instead of considering a splitting variable representation over the set of scenarios, we propose to decompose the model into a set of scenario clusters. We compare the computational performance of the four Lagrange multiplier updating procedures, namely the Subgradient Method, the Volume Algorithm, the Progressive Hedging Algorithm and the Dynamic Constrained Cutting Plane scheme for different numbers of scenario clusters and different dimensions of the original problem. Our computational experience shows that the CLD bound and its computational effort depend on the number of scenario clusters to consider. In any case, our results show that the CLD procedures outperform the traditional LD scheme for single scenarios both in the quality of the bounds and computational effort. All the procedures have been implemented in a C++ experimental code. A broad computational experience is reported on a test of randomly generated instances by using the MIP solvers COIN-OR and CPLEX for the auxiliary mixed 0-1 cluster submodels, this last solver within the open source engine COIN-OR. We also give computational evidence of the model tightening effect that the preprocessing techniques, cut generation and appending and parallel computing tools have in stochastic integer optimization. Finally, we have observed that the plain use of both solvers does not provide the optimal solution of the instances included in the testbed with which we have experimented but for two toy instances in affordable elapsed time. On the other hand the proposed procedures provide strong lower bounds (or the same solution value) in a considerably shorter elapsed time for the quasi-optimal solution obtained by other means for the original stochastic problem