Linear optimization (LO) is the fundamental problem of mathematical optimization. It admits an enormous number of applications in economics, engineering, science and many other fields. The three most significant classes of algorithms for solving LO problems are: Pivot, Ellipsoid and Interior Point Methods. Because Ellipsoid Methods are not efficient in practice we will concentrate on the comput...

The convergence of the Tapia indicators for infeasible{interior{point methods for solving degenerate linear complementarity problems is investigated. A new estimate of the rate of convergence of the Tapia indicators for the indices where both primal and dual variables vanish in the solution is obtained, showing that Tapia indicators for these indices converge slower than for other indices. Use ...

1. Introduction This document is the rst year progress report on the optimization projects funded by NSF Grant DDM-8922636. The projects principally include the interior-point algorithms for linear programming (LP), quadratic programming (QP), linear complementarity problem (LCP), and nonlinear programming (NP). The anticipated discoveries and advances resulting from the project include the fol...

In this paper, we show that the moving directions of the primal-affine scaling method (with logarithmic barrier function), the dual-affine scaling method (with logarithmic barrier function), and the primal-dual interior point method are merely the Newton directions along three different algebraic "paths" that lead to a solution of the Karush-Kuhn-Tucker conditions of a given linear programming ...

Interior point methods for nonlinear programs (NLP) are adapted for solution of mathematical programs with complementarity constraints (MPCCs). The constraints of the MPCC are suitably relaxed so as to guarantee a strictly feasible interior for the inequality constraints. The standard primal-dual algorithm has been adapted with a modified step calculation. The algorithm is shown to be superline...

Trust{Region Interior{Point Algorithms for a Class of Nonlinear Programming Problems by Lu s Nunes Vicente This thesis introduces and analyzes a family of trust{region interior{point (TRIP) reduced sequential quadratic programming (SQP) algorithms for the solution of minimization problems with nonlinear equality constraints and simple bounds on some of the variables. These nonlinear programming...

in this paper, we deal to obtain some new complexity results for solving semidefinite optimization (sdo) problem by interior-point methods (ipms). we define a new proximity function for the sdo by a new kernel function. furthermore we formulate an algorithm for a primal dual interior-point method (ipm) for the sdo by using the proximity function and give its complexity analysis, and then we sho...

A polynomial complexity bound is established for an interior point path following algorithm for the monotone linear complementarity problem that is based on the Chen{Harker{Kanzow smoothing techniques. The fundamental diierence with the Chen{Harker and Kanzow algorithms is the introduction of a rescaled Newton direction. The rescaling requires the iterates to remain in the interior of the posit...

All forms of the simplex method reach the optimum by traversing a series of basic solutions. Since each basic solution represents an extreme point of the feasible region, the track followed by the algorithm moves around the boundary of the feasible region. In the worst case, it may be necessary to examine most if not all of the extreme points. This can be cripplingly inefficient given that the ...