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

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

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)

Distress intolerance and pain catastrophizing as mediating variables in PTSD and chronic noncancer pain comorbidity.

Objectives: Several studies have demonstrated posttraumatic stress disorder (PTSD) and chronic pain comorbidity. However, there is a lack of research on the psychological variables that might explain their co-occurrence. We investigated the mediating role of distress intolerance and pain catastrophizing in this relationship. Methods: A moderated mediation model was tested. The sample comprised 114 individuals with chronic noncancer pain (90 women and 24 men; mean age, of 60.04 years [SD=9.76]). Results: Catastrophizing had a significant effect on PTSD. Distress intolerance mediated catastrophizing and PTSD, and pain intensity moderated this relationship. Conclusions: New insights are provided into the psychological variables that may explain PTSD and chronic noncancer pain comorbidity

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

A two-stage stochastic integer programming approach

We present an algorithmic approach for solving two-stage stochastic mixed 0-1 problems. The first stage constraints of the Deterministic Equivalent Model have 0--1 variables and continuous variables. The approach uses the Twin Node Family (TNF) concept within the algorithmic framework so-called {Branch-and-Fix Coordination} for satisfying the {nonanticipativity} constraints, jointly with a Benders Decomposition scheme for solving a given {LP} model at each {TNF} integer set. As an illustrative case, the structuring of a portfolio of Mortgage-Backed Securities under uncertainty in the interest rate path along a given time horizon is used. Some computational experience is reported.This research has been partially support by the grant Grupo consolidado de alto rendimiento 9/UPV 00038.321-13631/2001 from UPV, the project MEC2001-0636 from the DGCIT, the Researchers’ Education grant program 2000 from Gobierno Vasco, and the grant GRUPOS79/04 from the Generalitat Valenciana, Spain

Some experiments on solving multistage stochastic mixed 0-1 programs with time stochastic dominance constraints

In this work we extend to the multistage case two recent risk averse measures for two-stage stochastic programs based on first- and second-order stochastic dominance constraints induced by mixed-integer linear recourse. Additionally, we consider Time Stochastic Dominance (TSD) along a given horizon. Given the dimensions of medium-sized problems augmented by the new variables and constraints required by those risk measures, it is unrealistic to solve the problem up to optimality by plain use of MIP solvers in a reasonable computing time, at least. Instead of it, decomposition algorithms of some type should be used. We present an extension of our Branch-and-Fix Coordination algorithm, so named BFC-TSD, where a special treatment is given to cross scenario group constraints that link variables from different scenario groups. A broad computational experience is presented by comparing the risk neutral approach and the tested risk averse strategies. The performance of the new version of the BFC algorithm versus the plain use of a state-of-the-artMIP solver is also reported

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.This research has been partially supported by the project ECO2008-00777 ECON from the Spanish Ministry of Education and Science, and Grupo de Investigación IT-347-10 from the Basque Government

Efecto de la Terapia Hormonal de Reemplazo Sobre la Funcion Cognitiva en la Menopausia

Objetivo: Determinar el efecto de la terapia de reemplazo hormonal (TRH) sobre la función cognitiva en mujeres postmenopáusicas. Métodos: Del registro de participantes en el “Estudio Maracaibo del Envejecimiento”, se seleccionaron 72 mujeres menopáusicas, entre 55 y 79 años. Treinta y seis mujeres fueron tratadas con TRH combinada continua con estrógenos equinos conjugados (EEC) y acetato de medroxiprogesterona (AMP) durante un año, y 36 mujeres no recibieron THR: controles, pareados. El grupo1: recibió EEC 0,625mg + AMP 2,5mg; el grupo2: EEC 1,25mg + AMP 5,0mg. A todas las mujeres se le aplicaron pruebas neuropsicológicas, antes y después del tratamiento. Resultados: Al comparar cada grupo experimental y control consigo mismo al inicio y luego de 12 meses, se observaron cambios en el área de la memoria, especialmente acentuada en el grupo control. También se compararon las mediciones de cada grupo experimental con su respectivo grupo control luego de un año de la evaluación inicial, se observaron diferencias significativas a favor del grupo control 2 en las pruebas de memoria de reconocimiento, orientación y razonamiento . Conclusiones: Los cambios en la función cognitiva, observados en esta muestra, no pueden ser atribuidos a la TRH. - Objective: to evaluate the effect of hormone replacement therapy (HRT) on cognitive function in posmenopausal women. Methods: seventy two posmenopausal women aged 55-79 years were selected from the Aging Maracaibo Study. Thirty-six women were treated with combined continued HRT (conjugated equine estrogen-CEE plus medroxyprogesterone acetate-MPA) during one year: Group 1 were treated with CEE 0,625 mg plus MPA 2,5 mg; Group 2 were treated with CEE 1,25 mg plus MPA 5 mg. Thirty-six not treated women served as paired controls. Neuropsycological tests were performed in all women before and after treatment. Results: after one year of treatment with any of the CEE/MPA regimens, no significant changes in memory, recognition, orientation and rationalization were noted as compared with paired control groups. Unexpectedly, in these controls, beneficial effects on cognitive parameters were observed. Conclusion: HRT did not changed cognitive function after one year of follow-up in posmenopausal women