A priori bounds on the condition numbers in interior-point methods

Interior-point methods are known to be sensitive to ill-conditioning and to scaling of the data. This paper presents new asymptotically sharp bounds on the condition numbers of the linear systems at each iteration of an interior-point method for solving linear or semidefinite programs and discusses a stopping test which leads to a problem-independent “a priori” … Read more

Calibration by Optimization Without Using Derivatives

Applications in engineering frequently require the adjustment of certain parameters. While the mathematical laws that determine these parameters often are well understood, due to time limitations in every day industrial life, it is typically not feasible to derive an explicit computational procedure for adjusting the parameters based on some given measurement data. This paper aims … Read more

Simple examples for the failure of Newton’s method with line search for strictly convex minimization

In this paper two simple examples of a twice continuously differentiable strictly convex function $f$ are presented for which Newton’s method with line search converges to a point where the gradient of $f$ is not zero. The first example uses a line search based on the Wolfe conditions. For the second example, some strictly convex … Read more

Unifying semidefinite and set-copositive relaxations of binary problems and randomization techniques

A reformulation of quadratically constrained binary programs as duals of set-copositive linear optimization problems is derived using either \(\{0,1\}\)-formulations or \(\{-1,1\}\)-formulations. The latter representation allows an extension of the randomization technique by Goemans and Williamson. An application to the max-clique problem shows that the max-clique problem is equivalent to a linear program over the max-cut … Read more

A symmetric reduction of the NT direction

A stable symmetrization of the linear systems arising in interior-point methods for solving linear programs is introduced. A comparison of the condition numbers of the resulting interior-point linear systems with other commonly used approaches indicates that the new approach may be best suitable for an iterative solution. It is shown that there is a natural … Read more

On the cp-rank and minimal cp factorizations of a completely positive matrix

We show that the maximal cp-rank of $n\times n$ completely positive matrices is attained at a positive-definite matrix on the boundary of the cone of $n\times n$ completely positive matrices, thus answering a long standing question. We also show that the maximal cp-rank of $5\times 5$ matrices equals six, which proves the famous Drew-Johnson-Loewy conjecture … Read more

On Nesterov’s Smooth Chebyshev-Rosenbrock Function

We discuss a modification of the chained Rosenbrock function introduced by Nesterov, a polynomial of degree four of $n$ variables. Its only stationary point is the global minimizer with optimal value zero. An initial point is given such that any continuous piecewise linear descent path consists of at least an exponential number of $0.72 \cdot … Read more

High accuracy solution of large scale semidefinite programs

We present a first order approach for solving semidefinite programs. Goal of this approach is to compute a solution of the SDP up to high accuracy in spite of using only partial second order information. We propose a hybrid approach that uses an accelerated projection method to generate an approximate solution and then switches to … Read more

Solving large scale problems over the doubly nonnegative cone

The recent approach of solving large scale semidefinite programs with a first order method by minimizing an augmented primal-dual function is extended to doubly nonnegative programs. Regularity of the augmented primal-dual function is established under the condition of uniqueness and strict complementarity. The application to the doubly nonnegative cone is motivated by the fact that … Read more

Burer’s Key Assumption for Semidefinite and Doubly Nonnegative Relaxations

Burer has shown that completely positive relaxations of nonconvex quadratic programs with nonnegative and binary variables are exact when the binary variables satisfy a so-called key assumption. Here we show that introducing binary variables to obtain an equivalent problem that satisfies the key assumption will not improve the semidefinite relaxation, and only marginally improve the … Read more