Optimization and Dynamical Systems
by U. Helmke, J. B. Moore
Publisher: Springer 1996
Number of pages: 414
This work is aimed at mathematics and engineering graduate students and researchers in the areas of optimization, dynamical systems, control systems, signal processing, and linear algebra. The problems solved are those of linear algebra and linear systems theory, and include such topics as diagonalizing a symmetric matrix, singular value decomposition, balanced realizations, linear programming, sensitivity minimization, and eigenvalue assignment by feedback control.
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by Katta G. Murty - Springer
This is a Junior level book on some versatile optimization models for decision making in common use. The aim of this book is to develop skills in mathematical modeling, and in algorithms and computational methods to solve and analyze these models.
by Sebastien Bubeck - arXiv.org
This text presents the main complexity theorems in convex optimization and their algorithms. Starting from the fundamental theory of black-box optimization, the material progresses towards recent advances in structural and stochastic optimization.
by K.J.H. Law, A.M. Stuart, K.C. Zygalakis - arXiv.org
This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation. Authors develop a framework in which a Bayesian formulation of the problem provides the bedrock for the derivation and analysis of algorithms.
by P.-A. Absil, R. Mahony, R. Sepulchre - Princeton University Press
Many science and engineering problems can be rephrased as optimization problems on matrix search spaces endowed with a manifold structure. This book shows how to exploit the structure of such problems to develop efficient numerical algorithms.