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Optimization Models
Emphasizing practical understanding over the technicalities of specific algorithms, this elegant textbook is an accessible introduction to the field of optimization, focusing on powerful and reliable convex optimization techniques. Students and practitioners will learn how to recognize, simplify, model and solve optimization problems - and apply these principles to their own projects. A clear and self-contained introduction to linear algebra demonstrates core mathematical concepts in a way that is easy to follow, and helps students to understand their practical relevance. Requiring only a basic understanding of geometry, calculus, probability and statistics, and striking a careful balance between accessibility and rigor, it enables students to quickly understand the material, without being overwhelmed by complex mathematics. Accompanied by numerous end-of-chapter problems, an online solutions manual for instructors, and relevant examples from diverse fields including engineering, data science, economics, finance, and management, this is the perfect introduction to optimization for undergraduate and graduate students.
- GenresMathematics
651 pages, Kindle Edition
First published August 15, 2014
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Displaying 1 of 1 review
December 10, 2016
I skimmed through the book to get an overall idea of LP and surrounding. I liked the linear algebra part, which presents the subject by emphasizing how its various aspect reduce to various least square problems: the rule for the scalar product can be derived from the solution of the optimization problem of projecting one point on a given direction, PSD matrices are a natural notation for quadratic forms, eigenvalues and eigenvectors can be defined as orthogonal optima of the aforementioned quadratic forms, and finally the SVD can be seen as a "orthogonal maximizations" of the linear transformation on the unit sphere. In the book this perspective is more implicit than it may appear from my words, but shine through the many nice examples.
As for the optimization part, I found it less enjoyable: the various topics seems quite sketched and less well organized than the linear algebra part. This may be due to the fact that I was already familiar with linear algebra but unfamiliar with the theory of optimization, so the contrast of easiness may have biased my perception of quality.
Finally, you can read for free a great part of the material in the book at http://livebooklabs.com/keeppies/c5a5... (new version of https://inst.eecs.berkeley.edu/~ee127..., which contains many typos).
As for the optimization part, I found it less enjoyable: the various topics seems quite sketched and less well organized than the linear algebra part. This may be due to the fact that I was already familiar with linear algebra but unfamiliar with the theory of optimization, so the contrast of easiness may have biased my perception of quality.
Finally, you can read for free a great part of the material in the book at http://livebooklabs.com/keeppies/c5a5... (new version of https://inst.eecs.berkeley.edu/~ee127..., which contains many typos).
Displaying 1 of 1 review