Application of lower-bound direct method to large-scale problems

Application of Metaheuristics to Large-Scale Transportation Problems

A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion

We address the solution of large-scale statistical inverse problems in the framework of Bayesian inference. The Markov chain Monte Carlo (MCMC) method is the most popular approach for sampling the posterior probability distribution that describes the solution of the statistical inverse problem. MCMC methods face two central difficulties when applied to large-scale inverse problems: first, the f...

A Subspace, Interior, and Conjugate Gradient Method for Large-Scale Bound-Constrained Minimization Problems

BOUND-CONSTRAINED MINIMIZATION PROBLEMS MARY ANN BRANCH , THOMAS F. COLEMAN AND YUYING LI Abstract. A subspace adaptation of the Coleman-Li trust region and interior method is proposed for solving large-scale bound-constrained minimization problems. This method can be implemented with either sparse Cholesky factorization or conjugate gradient computation. Under reasonable conditions the converg...

The application of DEA and queuing models to large scale computer networks

A New Quantum Lower Bound Method, with an Application to a Strong Direct Product Theorem for Quantum Search

We present a new method for proving lower bounds on quantum query algorithms. The new method is an extension of the adversary method, by analyzing the eigenspace structure of the problem. Using the new method, we prove a strong direct product theorem for quantum search. This result was previously proved by Klauck, Špalek, and de Wolf (FOCS’04) using the polynomials method. No proof using the ad...