Applying an interior-point method to the central-path conditions is a widely used approach for solving quadratic programs. Reformulating these in log-domain natural variation on this that our knowledge previously unstudied. In paper, we analyze methods and prove their polynomial-time convergence. We also they are approximated by classical barrier precise sense provide simple computational exper...

The object of this paper is to perform an analysis of the sensitivity for convex vector programs with inequality constraints by examining the quantitative behavior of a certain set of optima according to changes of right-hand side parameters included in the program. The results in the paper prove that the sensitivity of the program depends on the solution of a dual program and its sensitivity. ...

The problem we concentrate on is as follows: given (1) a convex compact set X in R, an affine mapping x 7→ A(x), a parametric family {pμ(·)} of probability densities and (2) N i.i.d. observations of the random variable ω, distributed with the density pA(x)(·) for some (unknown) x ∈X, estimate the value gx of a given linear form at x. For several families {pμ(·)} with no additional assumptions o...

In this paper we present that solution of convex programming problems is equivalent with solution of projection formulation, then we introduce neural network models for solving projection formulation and analysis stability conditions and convergence. Simulation shows that the introduced neural network is effective in solving convex programming problems. 2006 Published by Elsevier Inc.

We study a generic minimization problem with separable non-convex piecewise linear costs, showing that the linear programming (LP) relaxation of three textbook mixed-integer programming formulations each approximates the cost function by its lower convex envelope. We also show a relationship between this result and classical Lagrangian duality theory.