Description:
Editorial Reviews
About the Author
Tim A. Wheeler wrote his PhD thesis on safety validation for autonomous vehicles and is now in industry working on air taxis.
Reviews:
5.0 out of 5 stars comprehensive and clear
This book was mind blowing.It covers around 100 different algorithms for optimization. Probably more; I didn't count thoroughly.It describes algorithms and concepts with incredible clarity and extreme concision.It builds progressively from simple to complex.It provides all the background information needed beyond a basic calculus class and some basic background dealing with matrices and vectors.It provides code snippets written in Julia of all the algorithms.It includes exercises and answers. Other examples are presented throughout the text.It provides resources online that run using Jupyter notebook with a Julia kernel.This book refreshed my memory and introduced me to so many topics. In particular, I found the sections on automatic differentiation, computational graphs, optimization under constraints, multiobjective optimization, surrogate models, sampling plans, and expression optimization to be enlightening and in some cases revolutionary to me. Like, OMG, you can do that? Over and over I thought, "I'll just skip this section. It seems irrelevant to what I need to learn." And each time I thought that, I'd start reading the section and would get hooked. Almost every section was highly relevant and provided building blocks for a deeper understanding. The book clarified so may ideas for me: function approximation, Lagrange multipliers and their extensions, duality, Pareto optimality, uses of quasi-random sequences, surrogate models, and probabilistic grammars. All of these ideas will be useful in my current projects.Julia was new to me. This language seems to be able to represent many loop structures and iteration processes in extremely compact form. Downloading and installing it and all other Julia modules used by the book was straightforward (except the Vec package needed a bit more sleuthing to get).Don't be fooled, though. This is an introductory text, and based on the preface, it appears to be intended for undergraduate-level courses. You will not find proofs of the results presented in the book - that is not the goal of the book. Margin notes provide relevant references from the primary (and secondary!) literature. For example, I had to look up more about probabilistic prototype trees and learning algorithms for these structures; it was a snap to find the relevant primary literature. The book's real strength is in the sheer number of algorithms described.Despite the comprehensive coverage, not all topics I was expecting were covered. I was hoping for something about expectation maximization and other latent variable methods. I also was hoping for more information about optimization with decision trees. Also, MCMC was missing although some Monte Carlo approaches were described; usually, the book advocated other methods over Monte Carlo approaches for more efficient optimization. Granted, this book is not intended as a machine learning book that might cover these missing topics in more detail. (BTW, the methods in the book can certainly be applied to machine learning problems. )The book sort of just ends. A final synthesis chapter that provides tables of the strengths, weaknesses, and areas of applicability of all the methods covered in the book, or a chapter outlining current challenges and areas of research, would be icing on the cake. The reader must make this synthesis themselves. Strengths and weaknesses are covered during the exposition of the various approaches, so this synthesis could be done with some discipline on the part of the reader.
5.0 out of 5 stars Direct and to the point book on optimization
So there is the old adage that if you give a man a fish he will eat once, but if you teach a man to fish he will eat forever. This book will definitely get you catching fish, but maybe leaves out how to clean and prepare the fish after it has been caught.When flipping through the book through the preview feature, it looked like the book just went straight to the matter of explaining the algorithms (which is great) and giving examples of each algorithm written in Julia (more on this later). I have only read about half of the book so far, and I would say the material is written to get you up and going quickly with algorithms for optimization and have been impressed so far.I will contrast this book to Nocedal and Wright (the only other optimization book that I own), and relate it to my opening paragraph. Nocedal and Wright is a really tough book to read. For better or worse, it focuses on some the excruciating details of many of the algorithms. There are many proofs, and generally does not deliver on giving something that you can code up quickly. This book will get you going quickly, but it skips much of nuanced formalism of Nocedal and Wright. This might mean that if some of the algorithms aren't working from this book, the explanations in this book may not be sufficient to make a truly robust solution for your problem. So in this regard, I see these two books complementing each other; one will deliver working pseudo code, and the other will provide a much more detailed description of the theory.Another reason that I was interested in this book, is that its algorithms are written in Julia; a language that seems intriguing given what I do for a living, which is signal processing and algorithm development. Because of this, I do a lot of work in Matlab. Matlab is for the most part pretty good if you know how to vectorize operations, but as soon as you need to string for loops together, you take a significant time penalty. Julia borrows syntax and concepts from Python and Matlab, and is a JIT complied language as well. However, it complies supposedly to machine code and should run pretty quickly. So given this, it has piqued my interest on learning Julia. This books seemed like it would fulfill these two needs of mine in one book, so win-win.Other feature of this book:- It looks like all the solutions for the end of chapter problems are solved:- It provides code for many standard functions used to test optimization algorithms- It provides a small tutorial for Julia in the appendixOverall this books is great and recommended.
5.0 out of 5 stars Great survey of what is available but lacking rigor and depth. Recomended!
This book is worth reading. As someone already pointed out, it is a huge gallery of algorithms of all kinds. Explanations and derivations are sometimes too brief, however it covers a variety of modifications, some of them being very niche. Thus if you consider the book as a survey of different strategies or you find yourself wandering on you problem unable to decide or even find good systematic way, chances that you find an inspiration here are indeed very high. I use this book as a first look, but due to lack of depth, I always navigate elsewhere to gain more. This way I discovered whole new realm of Bayesian approach and was blown away. Of course I ended up with Garnett book on that, but you get the point. This book gives you ideas and I highly recomend purchasing it.
5.0 out of 5 stars My favorite optimization book
There aren't really any books that combine excellent visualization, concise english intuition and brief code. I learnt optimization the hard way with far more opaque books and I wish this book existed when I was an optimization beginner.Part of what makes this book great is that the Julia code often mirrors the math almost identically so you never feel like implementing is a slog, you look at the formula, you type it in code and things just work.At the end of the day optimization is a numerical field and treating code as a first class citizen makes the material far more accesible than books than claim to be more formal by only treating the material in a rigorous but opaque mathematical manner.
Genius and compact book
It's a great book which guides carefully through the different level of optimization. Examples and exercises are useful and can be easily adapted for own software projects.
El mejor libro de optimización.
Este libro simplemente es increíble. Presenta temas muy avanzados de una forma clara y practica. Incluye los códigos de julia.
Example algorithm codes are writen in Julia language
Julia is not as common as other programming languages like Python, Matlab, making examples harder to follow for me.
Beschädigt
Das Buch selber ist sehr gut und verständlich geschrieben. Leider kam es beschädigt an, etliche Seiten sind geknickt oder eingedrückt.
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Algorithms for Optimization (Mit Press)
AED65214
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Visit the The MIT Press Store
Algorithms for Optimization (Mit Press)
AED65214
Quantity:
Order today to get by 7-14 business days
This item qualifies for free delivery
Imported From: United States
At BOLO, we work hard to ensure the products you receive are new, genuine, and sourced from reputable suppliers.
BOLO is not an authorized or official retailer for most brands, nor are we affiliated with manufacturers unless specifically stated on a product page. Instead, we source verified sellers, authorized distributors or directly from the manufacturer.
Each product undergoes thorough inspection and verification at our consolidation and fulfilment centers to ensure it meets our strict authenticity and quality standards before being shipped and delivered to you.
If you ever have concerns regarding the authenticity of a product purchased from us, please contact Bolo Support. We will review your inquiry promptly and, if necessary, provide documentation verifying authenticity or offer a suitable resolution.
Your trust is our top priority, and we are committed to maintaining transparency and integrity in every transaction.
All product information, images, descriptions, and reviews originate from the manufacturer or from trusted sellers overseas. BOLO is not affiliated with, endorsed by, or an authorized retailer for most brands listed on our website unless stated otherwise.
While we strive to display accurate information, variations in packaging, labeling, instructions, or formulation may occasionally occur due to regional differences or supplier updates. For detailed or manufacturer-specific information, please contact the brand directly or reach out to BOLO Support for assistance.
Unless otherwise stated, all prices displayed on the product page include applicable taxes and import duties.
BOLO operates in accordance with the laws and regulations of United Arab Emirates. Any items found to be restricted or prohibited for sale within the UAE will be cancelled prior to shipment. We take proactive measures to ensure that only products permitted for sale in United Arab Emirates are listed on our website.
All items are shipped by air, and any products classified as “Dangerous Goods (DG)” under IATA regulations will be removed from the order and cancelled.
All orders are processed manually, and we make every effort to process them promptly once confirmed. Products cancelled due to the above reasons will be permanently removed from listings across the website.
Description:
Editorial Reviews
About the Author
Tim A. Wheeler wrote his PhD thesis on safety validation for autonomous vehicles and is now in industry working on air taxis.
Reviews:
5.0 out of 5 stars comprehensive and clear
This book was mind blowing.It covers around 100 different algorithms for optimization. Probably more; I didn't count thoroughly.It describes algorithms and concepts with incredible clarity and extreme concision.It builds progressively from simple to complex.It provides all the background information needed beyond a basic calculus class and some basic background dealing with matrices and vectors.It provides code snippets written in Julia of all the algorithms.It includes exercises and answers. Other examples are presented throughout the text.It provides resources online that run using Jupyter notebook with a Julia kernel.This book refreshed my memory and introduced me to so many topics. In particular, I found the sections on automatic differentiation, computational graphs, optimization under constraints, multiobjective optimization, surrogate models, sampling plans, and expression optimization to be enlightening and in some cases revolutionary to me. Like, OMG, you can do that? Over and over I thought, "I'll just skip this section. It seems irrelevant to what I need to learn." And each time I thought that, I'd start reading the section and would get hooked. Almost every section was highly relevant and provided building blocks for a deeper understanding. The book clarified so may ideas for me: function approximation, Lagrange multipliers and their extensions, duality, Pareto optimality, uses of quasi-random sequences, surrogate models, and probabilistic grammars. All of these ideas will be useful in my current projects.Julia was new to me. This language seems to be able to represent many loop structures and iteration processes in extremely compact form. Downloading and installing it and all other Julia modules used by the book was straightforward (except the Vec package needed a bit more sleuthing to get).Don't be fooled, though. This is an introductory text, and based on the preface, it appears to be intended for undergraduate-level courses. You will not find proofs of the results presented in the book - that is not the goal of the book. Margin notes provide relevant references from the primary (and secondary!) literature. For example, I had to look up more about probabilistic prototype trees and learning algorithms for these structures; it was a snap to find the relevant primary literature. The book's real strength is in the sheer number of algorithms described.Despite the comprehensive coverage, not all topics I was expecting were covered. I was hoping for something about expectation maximization and other latent variable methods. I also was hoping for more information about optimization with decision trees. Also, MCMC was missing although some Monte Carlo approaches were described; usually, the book advocated other methods over Monte Carlo approaches for more efficient optimization. Granted, this book is not intended as a machine learning book that might cover these missing topics in more detail. (BTW, the methods in the book can certainly be applied to machine learning problems. )The book sort of just ends. A final synthesis chapter that provides tables of the strengths, weaknesses, and areas of applicability of all the methods covered in the book, or a chapter outlining current challenges and areas of research, would be icing on the cake. The reader must make this synthesis themselves. Strengths and weaknesses are covered during the exposition of the various approaches, so this synthesis could be done with some discipline on the part of the reader.
5.0 out of 5 stars Direct and to the point book on optimization
So there is the old adage that if you give a man a fish he will eat once, but if you teach a man to fish he will eat forever. This book will definitely get you catching fish, but maybe leaves out how to clean and prepare the fish after it has been caught.When flipping through the book through the preview feature, it looked like the book just went straight to the matter of explaining the algorithms (which is great) and giving examples of each algorithm written in Julia (more on this later). I have only read about half of the book so far, and I would say the material is written to get you up and going quickly with algorithms for optimization and have been impressed so far.I will contrast this book to Nocedal and Wright (the only other optimization book that I own), and relate it to my opening paragraph. Nocedal and Wright is a really tough book to read. For better or worse, it focuses on some the excruciating details of many of the algorithms. There are many proofs, and generally does not deliver on giving something that you can code up quickly. This book will get you going quickly, but it skips much of nuanced formalism of Nocedal and Wright. This might mean that if some of the algorithms aren't working from this book, the explanations in this book may not be sufficient to make a truly robust solution for your problem. So in this regard, I see these two books complementing each other; one will deliver working pseudo code, and the other will provide a much more detailed description of the theory.Another reason that I was interested in this book, is that its algorithms are written in Julia; a language that seems intriguing given what I do for a living, which is signal processing and algorithm development. Because of this, I do a lot of work in Matlab. Matlab is for the most part pretty good if you know how to vectorize operations, but as soon as you need to string for loops together, you take a significant time penalty. Julia borrows syntax and concepts from Python and Matlab, and is a JIT complied language as well. However, it complies supposedly to machine code and should run pretty quickly. So given this, it has piqued my interest on learning Julia. This books seemed like it would fulfill these two needs of mine in one book, so win-win.Other feature of this book:- It looks like all the solutions for the end of chapter problems are solved:- It provides code for many standard functions used to test optimization algorithms- It provides a small tutorial for Julia in the appendixOverall this books is great and recommended.
5.0 out of 5 stars Great survey of what is available but lacking rigor and depth. Recomended!
This book is worth reading. As someone already pointed out, it is a huge gallery of algorithms of all kinds. Explanations and derivations are sometimes too brief, however it covers a variety of modifications, some of them being very niche. Thus if you consider the book as a survey of different strategies or you find yourself wandering on you problem unable to decide or even find good systematic way, chances that you find an inspiration here are indeed very high. I use this book as a first look, but due to lack of depth, I always navigate elsewhere to gain more. This way I discovered whole new realm of Bayesian approach and was blown away. Of course I ended up with Garnett book on that, but you get the point. This book gives you ideas and I highly recomend purchasing it.
5.0 out of 5 stars My favorite optimization book
There aren't really any books that combine excellent visualization, concise english intuition and brief code. I learnt optimization the hard way with far more opaque books and I wish this book existed when I was an optimization beginner.Part of what makes this book great is that the Julia code often mirrors the math almost identically so you never feel like implementing is a slog, you look at the formula, you type it in code and things just work.At the end of the day optimization is a numerical field and treating code as a first class citizen makes the material far more accesible than books than claim to be more formal by only treating the material in a rigorous but opaque mathematical manner.
Genius and compact book
It's a great book which guides carefully through the different level of optimization. Examples and exercises are useful and can be easily adapted for own software projects.
El mejor libro de optimización.
Este libro simplemente es increíble. Presenta temas muy avanzados de una forma clara y practica. Incluye los códigos de julia.
Example algorithm codes are writen in Julia language
Julia is not as common as other programming languages like Python, Matlab, making examples harder to follow for me.
Beschädigt
Das Buch selber ist sehr gut und verständlich geschrieben. Leider kam es beschädigt an, etliche Seiten sind geknickt oder eingedrückt.
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Similar items from “Intelligence & Semantics”
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Or share with link
https://www.bolo.ae/products/U0262039427