A Simple Primal-Dual Feasible Interior-Point Methodfor Nonlinear Programming with Monotone Descent

We propose and analyze a primal-dual interior point method of the “feasible” type, with the additional property that the objective function decreases at each iteration. A distinctive feature of the method is the use of different barrier parameter values for each constraint, with the purpose of better steering the constructed sequence away from non-KKT stationary … Read more

The Thirteen Spheres: A New Proof

The “thirteen spheres problem”, also known as the “Gregory-Newton problem” is to determine the maximum number of three-dimensional spheres that can simultaneously touch a given sphere, where all the spheres have the same radius. The history of the problem goes back to a disagreement between Isaac Newton and David Gregory in 1694. Using a combination … Read more

On the superlinear local convergence of a filter-SQP method

Transition to superlinear local convergence is shown for a modified version of the trust-region filter-SQP method for nonlinear programming introduced by Fletcher, Leyffer, and Toint [8]. Hereby, the original trust-region SQP-steps can be used without an additional second order correction. The main modification consists in using the Lagrangian function value instead of the objective function … Read more

”Cone-Free” Primal-Dual Path-Following and Potential Reduction Polynomial Time Interior-Point Methods

We present a framework for designing and analyzing primal-dual interior-point methods for convex optimization. We assume that a self-concordant barrier for the convex domain of interest and the Legendre transformation of the barrier are both available to us. We directly apply the theory and techniques of interior-point methods to the given good formulation of the … Read more

Optimisation of physical and financial power purchase portfolios

The deregulation of the European power market brings new sales prospects for the power-suppliers as well as an appreciable increase of entrepreneurial risks. In order to handle the novel price- and volume-risks the optimisation of decisionmaking under uncertain boundary conditions is of essential interest. The former task of resource management in energy-supply was the minimisation … Read more

Optimal Portfolios using Linear Programming Models

The classical Quadratic Programming formulation of the well known portfolio selection problem, is cumbersome, time consuming and relies on two important assumptions: (a) the expected return is multivariate normally distributed; (b) the investor is risk averter. This paper formulates two alternative models, (i) maximin, and (ii) minimization of absolute deviation. Data from a very simple … Read more

SDPARA : SemiDefinite Programming Algorithm PARAllel Version

Abstract: The SDPA (SemiDefinite Programming Algorithm) is known as efficient computer software based on primal-dual interior-point method for solving SDPs (Semidefinite Programs). In many applications, however, some SDPs become larger and larger, too large for the SDPA to solve on a single processor. In execution of the SDPA applied to large scale SDPs, the computation … Read more

STRONG LOWER BOUNDS FOR THE PRIZE COLLECTING STEINER PROBLEM IN GRAPHS

Given an undirected graph G with nonnegative edges costs and nonnegative vertex penalties, the prize collecting Steiner problem in graphs (PCSPG) seeks a tree of G with minimum weight. The weight of a tree is the sum of its edge costs plus the sum of the penalties of those vertices not spanned by the tree. … Read more

Hierarchical Network Design Using Simulated Annealing

The hierarchical network problem is the problem of finding the least cost network, with nodes divided into groups, edges connecting nodes in each groups and groups ordered in a hierarchy. The idea of hierarchical networks comes from telecommunication networks where hierarchies exist. Hierarchical networks are described and a mathematical model is proposed for a two … Read more