Layout design problem is one of the useful field of study used to increase the efficiency of sources in organizations. In order to achieve an appropriate layout design, it is necessary to define and solve the related nonlinear programming problems. Therefore, using computer in solving the related problems is important in the view of the researchers of this area of study. However, the designs pr...

We consider a proximal operator given by a quadratic function subject to bound constraints and give an optimization algorithm using the alternating direction method of multipliers (ADMM). The algorithm is particularly efficient to solve a collection of proximal operators that share the same quadratic form, or if the quadratic program is the relaxation of a binary quadratic problem.

The standard quadratic program (QPS) is minx∈∆ xT Qx, where ∆ ⊂ <n is the simplex ∆ = {x ≥ 0 | ni=1 xi = 1}. QPS can be used to formulate combinatorial problems such as the maximum stable set problem, and also arises in global optimization algorithms for general quadratic programming when the search space is partitioned using simplices. One class of “d.c.” (for “difference between convex”) boun...

A large number of heuristic search algorithms are available for function optimization. Each of these heuristics, e.g., simulated annealing, genetic algorithms, tabu search, etc., has been shown to be effective at finding good solutions efficiently. However, little work has been directed at determining what are the important problem characteristics for which one algorithm is more efficient than ...

Ant colony optimization (ACO) is a stochastic search method for solving NP-hard problems. The exploration versus exploitation dilemma rises in ACO search. Reactive max-min ant system algorithm is a recent proposition to automate the exploration and exploitation. It memorizes the search regions in terms of reactive heuristics to be harnessed after restart, which is to avoid the arbitrary explora...

A new active set algorithm for minimizing quadratic functions with separable convex constraints is proposed by combining the conjugate gradient method with the projected gradient. It generalizes recently developed algorithms of quadratic programming constrained by simple bounds. A linear convergence rate in terms of the Hessian spectral condition number is proven. Numerical experiments, includi...

QUADRATIC INVERSE EIGENVALUE PROBLEMS: THEORY, METHODS, AND APPLICATIONS Vadim Olegovich Sokolov, Ph.D. Department of Mathematical Sciences Northern Illinois University, 2008 Biswa Nath Datta, Director This dissertation is devoted to the study of quadratic inverse eigenvalue problems from theoretical, computational and applications points of view. Special attention is given to two important pra...

We are considering the application of the Augmented Lagrangian algorithms with quadratic penalty, to convex problems of quadratic programming. The problems of quadratic programming are composites of quadratic objective function and linear constraints. This important class of problems will be generated through the algorithm of sequential quadratic programming, where at each iteration the quadrat...

The paper deals with an M/M/2 Queueing Model working vacations and reneging of customers due to impatience. matrix geometric method is used find the distribution number in system. A cost function constructed obtain optimal value service rate optimize (minimize) using Quadratic Fit Search Method (QFSM). Further, effects on system's performance measures numerical analysis graphical representation...