The simplex algorithm travels, on the underlying polyhedron, from vertex to vertex until reaching an optimal vertex. With the same simplex framework, the proposed algorithm generates a series of feasible points (which are not necessarily vertices). In particular, it is exactly an interior point algorithm if the initial point used is interior. Computational experiments show that the algorithm ar...

In this paper we propose a new large-update primal-dual interior point algorithm for P∗(κ) linear complementarity problems (LCPs). We generalize the analysis of BER’s primal-dual interior point algorithm for LP to P∗(κ) LCPs. New search directions and proximity measures are proposed based on a new kernel function which has linear growth term. We showed that if a strictly feasible starting point...

In this paper, we study diagonally scaled gradient methods for simple-bound constrained optimization in a framework almost identical to that for unconstrained optimization, except that iterates are kept within the interior of the feasible region. We establish a satisfactory global convergence theory for such interior-point gradient methods applied to Lipschitz continuously differentiable functi...

In this work we present a new feasible direction algorithm for solving smooth nonlinear second-order cone programs. These problems consist of minimizing a nonlinear differentiable objective function subject to some nonlinear second-order cone constraints. Given a point interior to the feasible set defined by the nonlinear constraints, the proposed approach computes a feasible and descent direct...

Linear complementarity problem noted (LCP) becames in present the subject of many reseach interest because it arises in many areas and it includes the two important domains in optimization:the linear programming (LP) and the convex quadratic (CQP) programming. So the researchers aims to extend the results obtained in (LP) and (CQP) to (LCP). Differents classes of methods are proposed to solve (...

In this paper, the filter technique of Fletcher and Leyffer (1997) is used to globalize the primaldual interior-point algorithm for nonlinear programming, avoiding the use of merit functions and the updating of penalty parameters. The new algorithm decomposes the primal-dual step obtained from the perturbed first-order necessary conditions into a normal and a tangential step, whose sizes are co...

Hyperbolic polynomials have their origins in partial diierential equations. We show in this paper that they have applications in interior point methods for convex programming. Each homogeneous hyperbolic polynomial p has an associated open and convex cone called its hyperbolicity cone. We give an explicit representation of this cone in terms of polynomial inequalities. The function F (x) = ? lo...

We present a new interactive multiobjective linear programming algorithm that is based on one variant of Karmarkar’s algorithm known as the path-following primal-dual algorithm. The modification of this single-objective linear programming algorithm to the multiobjective case is done by deriving an approximate gradient to the implicitly-known utility function. By interacting with the decision ma...

In this paper, the filter technique of Fletcher and Leyffer (1997) is used to globalize the primaldual interior-point algorithm for nonlinear programming, avoiding the use of merit functions and the updating of penalty parameters. The new algorithm decomposes the primal-dual step obtained from the perturbed first-order necessary conditions into a normal and a tangential step, whose sizes are co...