Spectral threshold dominance, Brouwer's conjecture and maximality of Laplacian energy

The Laplacian energy of a graph is the sum of the distances of the eigenvalues of the Laplacian matrix of the graph to the graph's average degree. The maximum Laplacian energy over all graphs on $n$ nodes and $m$ edges is conjectured to be attained for threshold graphs. We prove the conjecture to hold for graphs with the property that for each $k$ there is a threshold graph on the same number of nodes and edges whose sum of the $k$ largest Laplacian eigenvalues exceeds that of the $k$ largest Laplacian eigenvalues of the graph. We call such graphs spectrally threshold dominated. These graphs include split graphs and cographs and spectral threshold dominance is preserved by disjoint unions and taking complements. We conjecture that all graphs are spectrally threshold dominated. This conjecture turns out to be equivalent to Brouwer's conjecture concerning a bound on the sum of the $k$ largest Laplacian eigenvalues

Network Models with Convex Cost Structure like Bundle Methods

For three rather diverse applications (truck scheduling for inter warehouse logistics, university-course timetabling, operational train timetabling) that contain integer multi-commodity flow as a major modeling element we present a computational comparison between a bundle and a full linear programming (LP) approach for solving the basic relaxations. In all three cases computing the optimal solutions with LP standard solvers is computationally very time consuming if not impractical due to high memory consumption while bundle methods produce solutions of sufficient but low accuracy in acceptable time. The rounding heuristics generate comparable results for the exact and the approximate solutions, so this entails no loss in quality of the final solution. Furthermore, bundle methods facilitate the use of nonlinear convex cost functions. In practice this not only improves the quality of the relaxation but even seems to speed up convergence of the method

An accurate and efficient algorithm for the computation of the characteristic polynomial of a general square matrix

An algorithm is presented for the efficient and accurate computation of the coefficients of the characteristic polynomial of a general square matrix. The algorithm is especially suited for the evaluation of canonical traces in determinant quantum Monte-Carlo methods.Comment: 8 pages, no figures, to appear in J. Comp. phy

Exact Solution Methods for the kk-item Quadratic Knapsack Problem

The purpose of this paper is to solve the 0-1 $k$-item quadratic knapsack problem $(kQKP)$, a problem of maximizing a quadratic function subject to two linear constraints. We propose an exact method based on semidefinite optimization. The semidefinite relaxation used in our approach includes simple rank one constraints, which can be handled efficiently by interior point methods. Furthermore, we strengthen the relaxation by polyhedral constraints and obtain approximate solutions to this semidefinite problem by applying a bundle method. We review other exact solution methods and compare all these approaches by experimenting with instances of various sizes and densities.Comment: 12 page

A Preconditioned Iterative Interior Point Approach to the Conic Bundle Subproblem

The conic bundle implementation of the spectral bundle method for large scale semidefinite programming solves in each iteration a semidefinite quadratic subproblem by an interior point approach. For larger cutting model sizes the limiting operation is collecting and factorizing a Schur complement of the primal-dual KKT system. We explore possibilities to improve on this by an iterative approach that exploits structural low rank properties. Two preconditioning approaches are proposed and analyzed. Both might be of interest for rank structured positive definite systems in general. The first employs projections onto random subspaces, the second projects onto a subspace that is chosen deterministically based on structural interior point properties. For both approaches theoretic bounds are derived for the associated condition number. In the instances tested the deterministic preconditioner provides surprisingly efficient control on the actual condition number. The results suggest that for large scale instances the iterative solver is usually the better choice if precision requirements are moderate or if the size of the Schur complemented system clearly exceeds the active dimension within the subspace giving rise to the cutting model of the bundle method.Comment: 29+9 pages, 4 figure

Mathematicus scribens

Rede uitgesproken bij de aanvaarding van het ambt van buitengewoon hoogleraar in de analyse en theoretische waarschijnlijkheidsrekening aan de Technische Hogeschool Eindhoven op vrijdag 18 februari 196

Posterior Contraction Rates for the Bayesian Approach to Linear Ill-Posed Inverse Problems

We consider a Bayesian nonparametric approach to a family of linear inverse problems in a separable Hilbert space setting with Gaussian noise. We assume Gaussian priors, which are conjugate to the model, and present a method of identifying the posterior using its precision operator. Working with the unbounded precision operator enables us to use partial differential equations (PDE) methodology to obtain rates of contraction of the posterior distribution to a Dirac measure centered on the true solution. Our methods assume a relatively weak relation between the prior covariance, noise covariance and forward operator, allowing for a wide range of applications

A hybrid constraint programming and semidefinite programming approach for the stable set problem

This work presents a hybrid approach to solve the maximum stable set problem, using constraint and semidefinite programming. The approach consists of two steps: subproblem generation and subproblem solution. First we rank the variable domain values, based on the solution of a semidefinite relaxation. Using this ranking, we generate the most promising subproblems first, by exploring a search tree using a limited discrepancy strategy. Then the subproblems are being solved using a constraint programming solver. To strengthen the semidefinite relaxation, we propose to infer additional constraints from the discrepancy structure. Computational results show that the semidefinite relaxation is very informative, since solutions of good quality are found in the first subproblems, or optimality is proven immediately.Comment: 14 page

On a trigonometric inequality of Askey and Steinig.

A short proof is given for the inequalityd n sin kθdθX k=1 k sin θ/2 < 0 for 0 < θ < π,supplemented by a discussion of some related results.Mathematics Subject Classification: 51M16, 42A0, 42A2