Convex Optimization 1st Edition by Stephen Boyd | (PDF) Free Download

Description

Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Quite simply, this is a wonderful text. Coupling this with Boyd’s course at Stanford (the lecture videos, HWs, etc. are all available for free online), you’re bound to learn quite a lot about optimization. But most importantly, you’ll have an idea of when you can actually apply convex optimization to solve a problem that comes up in your particular field.My reasoning in giving it such praise is my preference for the rather unusual methodology it takes in introducing you to optimization. Most books I have seen on linear programming or non-linear programming tackle a few standard problems, introduce what is necessary in terms of definitions and proofs, and then focus on the algorithms that solve these standard problems (conjugate gradient et. al.), how they work, their pitfalls, etc. While this is undoubtedly useful material (which Boyd does cover for a good deal in the final chapters), the simple fact of the matter is these algorithms are available as standard methods in optimization packages (which are abstracted from the user), and unless you are actually going into developing, implementing and tweaking algorithms, this quite honestly is useless.What this book attempts to do, and does very well in my opinion, is to teach you to recognize convexity that’s present in problems that are first glance appear to be so incredibly removed from optimization that you might never consider it. This book spends the first 100 pages or so just devoted to building a “calculus” of convexity, if you will, so that you know through what operations convexity is preserved, and you develop intuition as to the potential to use convex optimization in problems in your particular field or application. As such, the first part of the books is focused on building up the skill set, the second part to applications of convex programming, and only the third to the actual algorithms.A word of warning: some of the explanations (especially in Chapter 4 which focuses on types of convex programs and equivalence of programs) are very general, which won’t be satisfying to certain readers who need solid examples to reinforce the concepts. Also, a lot of the material can be quite challenging, requiring a bit of mental gymnastics. However, if you are accompanying your study with the problems at the end of each chapter, you’re certain to get practice and demystify the concepts.In sum, all things considered, a great text.

⭐I just finished the reading of the entire book, and I feel that I am blessed that I have done this! The reading of this book is pure joy. I found all chapters easy to follow (whatever you needed was in the book, no need to look elsewhere) except possible Chapter 7 (statistical estimation). I found Chapter 10 and the 2nd half of chapter 11 the most difficult to read, but persistence will reward you. This book does not only teaches you Convex Optimization, but it also teaches you Matrix Analysis what-u-need-to-know, and after going through the examples, one gains enough knowledge about Matrix analysis to be able to apply it. There is also a free solution manual for the exercises of the book that someone should have handy because many exercises expand on the understanding of the concepts.

⭐I think this is the best book for getting into optimization. It’s simple with many examples and figures. Excellent choice for engineers, mathematicians might find it incomplete, but what can we do, that’s life. I think the interior point section could have had more, but it is still ok. The next step after this book is Nemirovski’s book “Lectures on Modern Convex optimization”. You can download it for free from his website […] along with many other notes. Nemirovski’s book is very complete and has very modern ideas new to many engineers. But as I said Boyd’s book is where you should start from. From an engineer’s perspective I believe Boyd’s book is much more easy to read and understand than Bertseka’s book

⭐. I also appreciate Boyd’s courtesy to have his book available on-line for free. I bought the book after downloading it because it is worth its price. Try also another book coming from Stanford, which is more specialized

⭐, also available on-line

⭐If you are a researcher or engineer in the field of optimization, you must read this book and have it on your desk at all times. Very lucid, clear chapters. I read it cover to cover, and keeping returning to it for refreshers and new insights.

⭐Can’t stop reading it !

⭐Got some torn pages. The sending was incredibly quick

⭐This material is somewhat tangential to my research, but I learned a ton by reading it. Very well organized.For example, here is a problem I was working on. For a given matrix A, find vectors a and b such that1. |A| <= ab^T, (outer product) and2. a^Tb (inner product) is a minimum. Convex Optimization showed me how to convert this into a CO problem ⭐This book is great! The writers tend to capture the theory of convex optimization in a concise way and further illustrate it by showing their applications. The book also has a rigorous set of exercises. ⭐As you begin your journey into machine learning, understand that the algorithms that you use stand on very firm ground, mostly in optimization. Most optimization problems can in general be thought of as solution finding in some Rn. Now turns out in certain classes of optimization, we can find some form of global optimum, and this class is the class of convex sets.Optimization is used everywhere, and all of us have used it already. A simple thing like f'(x) =0 is something that we have already used from school days. But the problem is twofold. Recognizing that a function in convex, for a general f, and then trying to solve f' = 0 in cases where f is not conducive to exact solutions.--This is a great first book for someone looking to enter the world of machine learning through optimization. This is another approach apart the statistical side (which is well covered in ESL by Hastie and Tibshiriani). Several fundamental things in machine learning like SVMs and gradient descent are based on the concepts learnt from optimization.One of the strengths of this book is that it doesn't jump into the solution methods or how to solve such problems until the problem is well understood. Spending more time in the problem space than the solution space let's the reader know why and when to apply the solutions suggested. The algorithms form the third (and last) part of the book. In a book like Fletcher, newtons method would be in the first chapter after the introduction. This approach too aids the new entrant to the field.I find this book a slightly easier read than the one by Bersetkas. That could be a good second book, before you move on to other topics based on your interest. If you are interested in finding solutions in Rn for general cases of f (say non convex), core optimization books like Luenberger or Fletcher may be recommendable, especially for numerical optimization enthusiasts. But these are only appreciated after a first pass through the subject. Nocedal and Wright is a wonderful book for someone with exposure to optimization.--As always I find the prints from Cambridge Press extremely readable and a pleasure to hold. I would recommend buying this book even though you may get online prints.--Problems in this book are hard. You may have to struggle a bit to solve the problems completely. This might affect your choice of whether to use this book as a textbook for convex optimization.--*Important*: Supplement the book by the highly recommended set of video lectures by the same Author (Boyd) on convex optimization available online. His conversational tone, and casual dropping of profound statements makes the video lectures some of the best I have seen.--Prerequisites: To appreciate the book, you need to have understood linear algebra (say atleast at level of Strang) as well as calculus (Joydeep Dutta recommended for this)--Overall:Recommended book in the library of every machine learning enthusiast. Not a must have, but almost there. ⭐nothing to complain ⭐A reference ⭐A great tool for the researcher. ⭐This book is beautiful!So many maths books are written by the geniuses for the geniuses, and this topic seems to abound in them. If however you are an ordinary mortal and have tried wading through these texts to understand convex functions and sets you will know how beautiful this book is and how awful most of the rest are, with dry definitions, incomplete or more frequently missing explanations and total lack of adequate illustration of this most visual of topics. Most also lack proper exercises to help develop instincts for the understanding and use of this very beautiful theory and the powerful and elegant though subtle duality properties that pervade it. This book does these things generously and properly.Given the great power of the assured convergence properties of convex function-based algorithms in a vast number of applications the topic is very poorly served by adequate texts and for my money this one is head and shoulders above the rest. And the natural primer to approaching the others with some confident understanding of what they're wittering about..Start saving your pocket money now!

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