Algorithms for Continuous Optimization, the State of the Art

This text presents the main algorithms for solving nonlinear continuous optimization (local and global methods), including linear programming as special cases (via simplex or interior point methods) and linear complementarity problems. Recently developed topics of parallel computation, neural networks for optimization, automatic differentiation and ABS methods are included. The book consists of 19 chapters written by well-known specialists, who have made major contributions to developing the field. While a few chapters are mainly theoretical (as the one by Giannessi, which provides a novel, far-reaching approach to optimality conditions, and the one by Spedicato, which presents the unifying tool given by the ABS approach) most chapters have been written with special attention to features like stability, efficiency, high performance and software availability. The book should be of interest to persons with both theoretical and practical interest in the important field of optimization.