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Numerical Optimization

4.21  ·  Rating details ·  97 ratings  ·  6 reviews
Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple i ...more
Hardcover, 664 pages
Published July 1st 2006 by Springer (first published April 28th 2000)
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Dan Boeriu
Jun 12, 2017 rated it it was amazing
Shelves: math
This is a reference book in the optimization field. The proofs may not be the most formal but they are clearly and well written. What I especially like in this book is the buildup for every concept and proof. It always starts with the general statement of a problem then a few examples that re-iterate some ideas used to solve each of the cases. Then it moves onto proving the solution for the general problem at which point you already have a sense of where this is going. I've rarely seen chapters ...more
Giuseppe Burtini
May 10, 2017 rated it it was amazing
Dense, timeless and comprehensive. The density and mathematical nature of the material means every page takes much longer to consume than you expect - a slow read, but fully capable of taking a post-calculus computer science student to being a fully competent optimizer.
Feb 18, 2018 rated it it was amazing
Best introduction and survey-reference for nonlinear programming. I like it substantially better than Bertsekas or Boyd/Vandenburghe, though it covers less theory than either.
Mar 20, 2012 rated it really liked it
Amazon currently has this book for around $60. At this price, I consider it an easy buy because of the wealth of information. Particularly good are chapters 12 and 5, which are about the theory of constrained optimization and conjugate gradient descent respectively. I've spent time on and off trying to understand the Karush-Kuhn-Tucker conditions (which is essentially the fundamental theorem of constrained optimization) without much success. After reading Chapter 12, I wondered why I struggled t ...more
Dec 24, 2012 rated it really liked it
Shelves: for-serious
An excellent text on the theory and algorithms of mathematical optimization, naturally focussing on convex problems. Its treatment is a bit more formal than some other texts I've seen (e.g., they delve into the depth of the analytic conditions necessary to really formally establish the KKT conditions), but at least they give some nice, visual examples of different conditions.

Not what I would recommend for "intuition building" in this space, but a good book for really getting to the whys behind a
Zarathustra Goertzel
Oct 21, 2016 rated it really liked it
We used this in a course, doing problems and as practicals matlab implementations.
I read chapters 1,2,3,4,6,10,11,12,16. (IIRC)

The book often requires cross-referencing, but the core intuitions behind how the algorithms work and the proofs of why are good. Examples are generally provided.
(While sometimes a bit confusing, there are enough details to use as pseudocode.)
Chapter 10 on the Levenberg–Marquardt algorithm was perhaps a bit rushed.
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