Problema de asignación quadrática (pac) sobre gpu a través de una pga maestro-esclavo

This document describes the implementation of a Master–Slave Parallel Genetic Algorithm (PGA) on Graphic Processing Units (GPU) to find solutions or solutions close to optimal solutions to particular instances of the Quadratic Assignment Problem (QAP). The efficiency of the algorithm is tested on a set of QAPLIB standard library problems.Este documento describe la implementación de un algoritmo genético paralelo maestroesclavo (AGP) en unidades de procesamiento gráfico (UPG) para encontrar soluciones o soluciones cercanas a soluciones óptimas para casos particulares del Problema de asignación Cuadrática (PAC). La eficiencia del algoritmo se prueba en un conjunto de problemas de la biblioteca estándar QAPLIB

Restricted Dynamic Programming Heuristic for Precedence Constrained Bottleneck Generalized TSP

We develop a restricted dynamical programming heuristic for a complicated traveling salesman problem: a) cities are grouped into clusters, resp. Generalized TSP; b) precedence constraints are imposed on the order of visiting the clusters, resp. Precedence Constrained TSP; c) the costs of moving to the next cluster and doing the required job inside one are aggregated in a minimax manner, resp. Bottleneck TSP; d) all the costs may depend on the sequence of previously visited clusters, resp. Sequence-Dependent TSP or Time Dependent TSP. Such multiplicity of constraints complicates the use of mixed integer-linear programming, while dynamic programming (DP) benefits from them; the latter may be supplemented with a branch-and-bound strategy, which necessitates a “DP-compliant” heuristic. The proposed heuristic always yields a feasible solution, which is not always the case with heuristics, and its precision may be tuned until it becomes the exact DP

Algorithms for Hierarchical and Semi-Partitioned Parallel Scheduling

We propose a model for scheduling jobs in a parallel machine setting that takes into account the cost of migrations by assuming that the processing time of a job may depend on the specific set of machines among which the job is migrated. For the makespan minimization objective, the model generalizes classical scheduling problems such as unrelated parallel machine scheduling, as well as novel ones such as semi-partitioned and clustered scheduling. In the case of a hierarchical family of machines, we derive a compact integer linear programming formulation of the problem and leverage its fractional relaxation to obtain a polynomial-time 2-approximation algorithm. Extensions that incorporate memory capacity constraints are also discussed

QuantityEr: Una solución simple y extensible para obtener la cantidad de resultados de consultas complejas a GitHub

GitHub is a platform that provides hosting for software development version control using Git. It features an application programming interface to allow the software to interact with the platform. The enormous quantity of information Hosted in GitHub may be useful to make studies about the current presence of development tools in the open-source software development community. However, the search engine has restrictions that make it impossible to issue complex queries to the platform. In this report, it is described as an object-oriented and extensible solution, named QuantityEr, to obtain the number of search results of complex queries to GitHub by using the inclusion-exclusion principle. The mathematical definitions, as well as related concepts, are presented. The mathematical model is discussed. The application of general design and used development tools are presented. Also, the results of the execution examples are showed. It is concluded that the treated problem has been solved although more work may be done to improve the solution.GitHub es una plataforma que proporciona alojamiento para el control de versiones de desarrollo de software utilizando Git. Cuenta con una interfaz de programación de aplicaciones para permitir que el software interactúe con la plataforma. La enorme cantidad de información alojada en GitHub puede ser útil para realizar estudios sobre la presencia actual de herramientas de desarrollo en la comunidad de desarrollo de software de código abierto. Sin embargo, el motor de búsqueda posee restricciones que hacen imposible emitir consultas complejas a la plataforma. En este informe, se describe una solución extensible y orientada a objetos, llamada QuantityEr, para obtener la cantidad de resultados de búsqueda de consultas complejas a GitHub utilizando el principio de inclusión-exclusión. Se presentan las definiciones matemáticas y los conceptos relacionados. Se discute el modelo matemático. Se presentan el diseño general de la aplicación y las herramientas de desarrollo utilizadas. Además, son mostrados resultados de ejemplos de ejecución. Se concluye que el problema tratado ha sido resuelto aunque se puede trabajar para mejorar la solución

Scheduling Parallel Jobs with Linear Speedup

We consider a scheduling problem where a set of jobs is distributed over parallel machines. The processing time of any job is dependent on the usage of a scarce renewable resource, e.g., personnel. An amount of k units of that resource can be allocated to the jobs at any time, and the more of that resource is allocated to a job, the smaller its processing time. The dependence of processing times on the amount of resources is linear for any job. The objective is to find a resource allocation and a schedule that minimizes the makespan. Utilizing an integer quadratic programming relaxation, we show how to obtain a (3+e)-approximation algorithm for that problem, for any e>0. This generalizes and improves previous results, respectively. Our approach relies on a fully polynomial time approximation scheme to solve the quadratic programming relaxation. This result is interesting in itself, because the underlying quadratic program is NP-hard to solve in general. We also briefly discuss variants of the problem and derive lower bounds.operations research and management science;