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FAQ:
Q: What is the best book to learn numerical optimization?
A: Numerical Optimization, 2006. If you do prefer the theoretical approach to the subject, another widely used mathematical book on optimization is “ Convex Optimization ” written by Stephen Boyd and Lieven Vandenberghe and published in 2004. This book was written by Andries Engelbrecht and published in 2007.
Q: Why are there so many books on optimization?
A: The field of optimization is enormous as it touches many other fields of study. As such, there are hundreds of books on the topic, and most are textbooks filed with math and proofs. This is fair enough given that it is a highly mathematical subject.
Q: Are there any books on optimization algorithms for machine learning?
A: Nevertheless, there are books that provide a more approachable description of optimization algorithms. Not all optimization algorithms are relevant to machine learning; instead, it is useful to focus on a small subset of algorithms. Frankly, it is hard to group optimization algorithms as there are many concerns.
Q: What are some examples of mathematical optimization?
A: Here are a few examples: Mathematical Optimization is a branch of applied mathematics which is useful in many different fields. Here are a few examples: Your basic optimization problem consists of… •The objective function, f(x), which is the output you’re trying to maximize or minimize.