In a parameter estimation problem with large numbers of unknown parameters, traditional algorithms such as fisher scoring and Newton-Raphson become impractical. A typical case is solution by discretization of linear inverse problems; an example is medical image reconstruction from projections....

This paper describes a three-step algorithm for estimating a system of error-correction equations that can be easily programmed using least-squares procedures. Nonetheless, the algorithm is both statistically and computationally efficient and when iterated gives maximum likelihood estimates of...

Besides scheduling problems for single and parallel machines and shop scheduling problems the book covers advanced models involving due-dates, sequence dependent changeover times and batching. Also multiprocessor task scheduling and problems with multipurpose machines are discussed. The methods...

We present in this paper a particular Metropolis-type algorithm for the simulation of a beta (formula) variable whose convergence is extremely slow. The interest of this phenomenon is to provide a simple benchmark against which convergence control techniques can be tested. We illustrate this use...

This book examines the mathematics, probability, statistics, and computational theory underlying neural networks and their applications. In addition to the theoretical work, the book covers a considerable range of neural network topics such as learning and training, neural network classifiers,...

This book explores the meta-heuristics approach called tabu search, which is dramatically changing our ability to solve a host of problems that stretch over the realms of resource planning, telecommunications, VLSI design, financial analysis, scheduling, space planning, energy distribution,...