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Problemas de óptimo que relacionan la información de Kullback y el conjunto de riesgos de Neyman-Pearson.

Ramiro Melendreras Gimeno (1983)

Trabajos de Estadística e Investigación Operativa

Consideramos la conexión que existe entre la información de Kullback y los tests admisibles óptimos en el conjunto de riesgos de Neyman-Pearson, usando para ello el estudio de problemas de programación matemática de tipo infinito. Se obtienen resultados que caracterizan un subconjunto de soluciones Bayes como consecuencia del conocimiento de la información, así como una medida de discriminación entre hipótesis para el conjunto de riesgos.

Proč řešit graficky úlohy lineárního programování

Andrea Kubišová (2016)

Učitel matematiky

At universities focused on economy, Operation Research topics are usually included in the study plan, including solving of Linear Programming problems. A universal tool for their algebraic solution is (numerically difficult) Simplex Algorithm, for which it is necessary to know at least the fundamental of Matrix Algebra. To illustrate this method of solving LP problems and to discuss all types of results, it seems to be very convenient to include a chapter about graphic solutions to LP problems....

Scheduling electric power generators using particle swarm optimization combined with the lagrangian relaxation method

Huseyin Balci, Jorge Valenzuela (2004)

International Journal of Applied Mathematics and Computer Science

This paper describes a procedure that uses particle swarm optimization (PSO) combined with the Lagrangian Relaxation (LR) framework to solve a power-generator scheduling problem known as the unit commitment problem (UCP). The UCP consists of determining the schedule and production amount of generating units within a power system subject to operating constraints. The LR framework is applied to relax coupling constraints of the optimization problem. Thus, the UCP is separated into independent optimization...

Scope and generalization of the theory of linearly constrained linear regulator

Paolo Alessandro, Elena de Santis (1999)

Kybernetika

A previous paper by the same authors presented a general theory solving (finite horizon) feasibility and optimization problems for linear dynamic discrete-time systems with polyhedral constraints. We derived necessary and sufficient conditions for the existence of solutions without assuming any restrictive hypothesis. For the solvable cases we also provided the inequative feedback dynamic system, that generates by forward recursion all and nothing but the feasible (or optimal, according to the cases)...

Selected multicriteria shortest path problems: an analysis of complexity, models and adaptation of standard algorithms

Zbigniew Tarapata (2007)

International Journal of Applied Mathematics and Computer Science

The paper presents selected multicriteria (multiobjective) approaches to shortest path problems. A classification of multi-objective shortest path (MOSP) problems is given. Different models of MOSP problems are discussed in detail. Methods of solving the formulated optimization problems are presented. An analysis of the complexity of the presented methods and ways of adapting of classical algorithms for solving multiobjective shortest path problems are described. A comparison of the effectiveness...

Sliding subspace design based on linear matrix inequalities

Alán Tapia, Raymundo Márquez, Miguel Bernal, Joaquín Cortez (2014)

Kybernetika

In this work, an alternative for sliding surface design based on linear and bilinear matrix inequalities is proposed. The methodology applies for reduced and integral sliding mode control, both continuous- and discrete-time; it takes advantage of the Finsler's lemma to provide a greater degree of freedom than existing approaches for sliding subspace design. The sliding surfaces thus constructed are systematically found via convex optimization techniques, which are efficiently implemented in commercially...

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