On cost matrices with two and three distinct values of Hamiltonian paths and cycles

Polynomially testable characterization of cost matrices associated with a complete digraph on $n$ nodes such that all the Hamiltonian cycles (tours) have the same cost is well known. Tarasov~\cite{TARA81} obtained a characterization of cost matrices where tour costs take two distinct values. We provide a simple alternative characterization of such cost matrices that can be … Read more

Symmetry Points of Convex Set: Basic Properties and Computational Complexity

Given a convex body S and a point x \in S, let sym(x,S) denote the symmetry value of x in S: sym(x,S):= max{t : x + t(x – y) \in S for every y \in S}, which essentially measures how symmetric S is about the point x, and define sym(S):=\max{sym(x,S) : x \in S }. … Read more

Subspace trust-region methods for large bound-constrained nonlinear equations

Trust-region methods for solving large bound-constrained nonlinear systems are considered. They allow for spherical or elliptical trust-regions where the search of an approximate solution is restricted to a low dimensional space. A general formulation for these methods is introduced and global and superlinear/quadratic convergence is shown under standard assumptions. Viable approaches for implementation in conjunction … Read more

Recovering Risk-Neutral Probability Density Functions from Options Prices using Cubic Splines

We present a new approach to estimate the risk-neutral probability density function (pdf) of the future prices of an underlying asset from the prices of options written on the asset. The estimation is carried out in the space of cubic spline functions, yielding appropriate smoothness. The resulting optimization problem, used to invert the data and … Read more

Time Offset Optimization in Digital Broadcasting

We investigate a planning problem arising in the forthcoming Digital Video Broadcasting (DVB-T) system. Unlike current analog systems, the DVB-T standard allows a mitigation of the interference by means of a suitable synchronization of the received signals. The problem we describe in this paper is that of finding a time offset to impose to the … Read more

A primal-dual nonlinear rescaling method with dynamic scaling parameter update

In this paper we developed a general primal-dual nonlinear rescaling method with dynamic scaling parameter update (PDNRD) for convex optimization. We proved the global convergence, established 1.5-Q-superlinear rate of convergence under the standard second order optimality conditions. The PDNRD was numerically implemented and tested on a number of nonlinear problems from COPS and CUTE sets. … Read more

Convergence analysis of a primal-dual interior-point method for nonlinear programming

We analyze a primal-dual interior-point method for nonlinear programming. We prove the global convergence for a wide class of problems under the standard assumptions on the problem. Citation Technical Report ORFE-04-07, Department of ORFE, Princeton University, Princeton, NJ 08544 Article Download View Convergence analysis of a primal-dual interior-point method for nonlinear programming

Numerical experiments with an interior-exterior point method for nonlinear programming

The paper presents an algorithm for solving nonlinear programming problems. The algorithm is based on the combination of interior and exterior point methods. The latter is also known as the primal-dual nonlinear rescaling method. The paper shows that in certain cases when the interior point method fails to achieve the solution with the high level … Read more

On exploiting structure induced when modelling an intersection of cones in conic optimization

Conic optimization is the problem of optimizing a linear function over an intersection of an affine linear manifold with the Cartesian product of convex cones. However, many real world conic models involves an intersection rather than the product of two or more cones. It is easy to deal with an intersection of one or more … Read more

Steered sequential projections for the inconsistent convex feasibility problem

We study a steered sequential gradient algorithm which minimizes the sum of convex functions by proceeding cyclically in the directions of the negative gradients of the functions and using steered step-sizes. This algorithm is applied to the convex feasibility problem by minimizing a proximity function which measures the sum of the Bregman distances to the … Read more