"Designed for graduate and advanced undergraduate students, this text provides a much-needed contemporary introduction to optimization. Emphasizing general problems and the underlying theory, it covers the fundamental problems of constrained and unconstrained optimization, linea…
"Designed for graduate and advanced undergraduate students, this text provides a much-needed contemporary introduction to optimization. Emphasizing general problems and the underlying theory, it covers the fundamental problems of constrained and unconstrained optimization, linea…
"Designed for graduate and advanced undergraduate students, this text provides a much-needed contemporary introduction to optimization. Emphasizing general problems and the underlying theory, it covers the fundamental problems of constrained and unconstrained optimization, linea…
Give Your Students the Proper Groundwork for Future Studies in OptimizationA First Course in Optimization is designed for a one-semester course in optimization taken by advanced undergraduate and beginning graduate students in the mathematical sciences and engineering. It teache…
This textbook offers graduate students a concise introduction to the classic notions of convex optimization. Written in a highly accessible style and including numerous examples and illustrations, it presents everything readers need to know about convexity and convex optimizatio…
Descripción del editor: "Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together…
This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see it…