--- product_id: 2966404 title: "Convex Optimization" price: "€ 159.07" currency: EUR in_stock: true reviews_count: 10 url: https://www.desertcart.at/products/2966404-convex-optimization store_origin: AT region: Austria --- # Convex Optimization **Price:** € 159.07 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Convex Optimization - **How much does it cost?** € 159.07 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.at](https://www.desertcart.at/products/2966404-convex-optimization) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description desertcart.com: Convex Optimization: 9780521833783: Boyd, Stephen, Vandenberghe, Lieven: Books Review: Good "first-pass" for convex optimization, but need to be comfortable with the basics. - This review is written from the perspective of a mathematically capable outsider to the field of Operations Research and Optimization that does research in statistics & machine learning. I have found that it's very difficult to figure out how to self-study these topics in general, so I'm going to include context & other resources I have found useful to highlight the importance and quality of this book. In my opinion, the landscape of OR and Optimization have to be understood to really appreciate it. When I initially picked up Boyd & Vanderberghe, I got very frustrated with it. I would recommend going through introductory optimization material first (I went through "Managerial Decision Modeling with Spreadsheets" by Balakrishnan et al, which is excellent), and be comfortable with the basics of linear algebra and calculus, otherwise this book will seem more inaccessible than it actually is. I have also heard that Stephen Boyd's lectures on YouTube are an excellent supplement to the book and make the introduction far more gentle. This book is a field manual for learning to recognize convex problems "in the wild": meaning, given an optimization problem, how do we make it easier to solve by making one or more parts of the problem convex? This process is known as "convex relaxation" and is one of the fundamental skills required for research in topics like statistics & machine learning, and probably many other fields as well. Because of this focus, the book strikes a very important middle ground between the highly applied side of case studies and model building, and the highly theoretical side of algorithms and convex analysis. It provides just enough theory to be able to work with problems, and just enough intuition on algorithms to know what's going on behind the scenes. I don't know why, but most treatments I have seen of Optimization/Operations Research is presented with too much of a focus on either theory/algorithms, or case studies with barely any focus on the former. After reading this text and doing the exercises (for which the solutions are freely available online), the reader can expect to be able to formulate optimization problems, relax those problems to be more easily solvable, input the problems to an optimization solver like CVX (Matlab) or CVXPY (CVX, but in Python) and have an idea of what algorithms the solver will use to solve the problem. From what I have seen so far, I believe this text alone is sufficient for a good initial background to do statistics & machine learning research. However, I don't believe it's enough for people who want to focus on the difficulties of Operations Research model building, writing optimization algorithms, or on theoretical topics like existence of solutions, convergence proofs for algorithms, etc. For discussion on the challenges associated with building big optimization models, I recommend H. Paul Williams's "Model Building in Mathematical Programming" (2013)--multiple real-world problems, their model formulations, and solutions are presented, and it's just amazing practice for a budding analyst. I think the introductory chapters are definitely lacking though, and I recommend picking up "Managerial Decision Modeling with Spreadsheets", as I mentioned above. For learning how to write numerical optimization algorithms, it is hard to go wrong with Nocedal's and Wright's "Numerical Optimization" (2006). The presentation is, like this book, crystal clear, but again requires the reader to have a good grasp on the basics of optimization, linear algebra, and calculus. The entire focus of the book is on algorithms to solve various optimization problems (not just convex problems!), but because of its age, it will not contain material on some of the newer algorithms that have found use. I would recommend looking through (free) online courses such as Ryan Tibshirani's "Machine Learning 10-725", Stephen Boyd's EE364A/EE364B sequence, and Emmanuel Candes's Math 301. Unfortunately, I am not the right person to ask for book recommendations to learn the theory, but I have heard good things about books by Nesterov, Bertsekas, and Rockafellar. These are books that really demand the reader to have a background in mathematical analysis. From my limited searching, Bertsekas's books have solution manuals, have good geometric descriptions of the concepts, and are self-contained. The books "Convex Optimization Theory" (2009), "Convex Optimization Algorithms" (2015), and "Convex Analysis & Optimization" (2003) in particular might be a good place to start. Review: The way to go for introducing optimization - 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. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #112,315 in Books ( See Top 100 in Books ) #1 in Linear Programming (Books) #28 in Mathematics Reference (Books) #64 in Probability & Statistics (Books) | | Customer Reviews | 4.6 4.6 out of 5 stars (208) | | Dimensions | 9.8 x 1.5 x 7.5 inches | | Edition | 1st | | ISBN-10 | 0521833787 | | ISBN-13 | 978-0521833783 | | Item Weight | 4.2 pounds | | Language | English | | Print length | 727 pages | | Publication date | March 8, 2004 | | Publisher | Cambridge University Press | ## Images ![Convex Optimization - Image 1](https://m.media-amazon.com/images/I/515b1NL1ObL.jpg) ![Convex Optimization - Image 2](https://m.media-amazon.com/images/I/71Vh9emz3sL.jpg) ![Convex Optimization - Image 3](https://m.media-amazon.com/images/I/31krkqkc5ZL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Good "first-pass" for convex optimization, but need to be comfortable with the basics. *by J***A on February 22, 2024* This review is written from the perspective of a mathematically capable outsider to the field of Operations Research and Optimization that does research in statistics & machine learning. I have found that it's very difficult to figure out how to self-study these topics in general, so I'm going to include context & other resources I have found useful to highlight the importance and quality of this book. In my opinion, the landscape of OR and Optimization have to be understood to really appreciate it. When I initially picked up Boyd & Vanderberghe, I got very frustrated with it. I would recommend going through introductory optimization material first (I went through "Managerial Decision Modeling with Spreadsheets" by Balakrishnan et al, which is excellent), and be comfortable with the basics of linear algebra and calculus, otherwise this book will seem more inaccessible than it actually is. I have also heard that Stephen Boyd's lectures on YouTube are an excellent supplement to the book and make the introduction far more gentle. This book is a field manual for learning to recognize convex problems "in the wild": meaning, given an optimization problem, how do we make it easier to solve by making one or more parts of the problem convex? This process is known as "convex relaxation" and is one of the fundamental skills required for research in topics like statistics & machine learning, and probably many other fields as well. Because of this focus, the book strikes a very important middle ground between the highly applied side of case studies and model building, and the highly theoretical side of algorithms and convex analysis. It provides just enough theory to be able to work with problems, and just enough intuition on algorithms to know what's going on behind the scenes. I don't know why, but most treatments I have seen of Optimization/Operations Research is presented with too much of a focus on either theory/algorithms, or case studies with barely any focus on the former. After reading this text and doing the exercises (for which the solutions are freely available online), the reader can expect to be able to formulate optimization problems, relax those problems to be more easily solvable, input the problems to an optimization solver like CVX (Matlab) or CVXPY (CVX, but in Python) and have an idea of what algorithms the solver will use to solve the problem. From what I have seen so far, I believe this text alone is sufficient for a good initial background to do statistics & machine learning research. However, I don't believe it's enough for people who want to focus on the difficulties of Operations Research model building, writing optimization algorithms, or on theoretical topics like existence of solutions, convergence proofs for algorithms, etc. For discussion on the challenges associated with building big optimization models, I recommend H. Paul Williams's "Model Building in Mathematical Programming" (2013)--multiple real-world problems, their model formulations, and solutions are presented, and it's just amazing practice for a budding analyst. I think the introductory chapters are definitely lacking though, and I recommend picking up "Managerial Decision Modeling with Spreadsheets", as I mentioned above. For learning how to write numerical optimization algorithms, it is hard to go wrong with Nocedal's and Wright's "Numerical Optimization" (2006). The presentation is, like this book, crystal clear, but again requires the reader to have a good grasp on the basics of optimization, linear algebra, and calculus. The entire focus of the book is on algorithms to solve various optimization problems (not just convex problems!), but because of its age, it will not contain material on some of the newer algorithms that have found use. I would recommend looking through (free) online courses such as Ryan Tibshirani's "Machine Learning 10-725", Stephen Boyd's EE364A/EE364B sequence, and Emmanuel Candes's Math 301. Unfortunately, I am not the right person to ask for book recommendations to learn the theory, but I have heard good things about books by Nesterov, Bertsekas, and Rockafellar. These are books that really demand the reader to have a background in mathematical analysis. From my limited searching, Bertsekas's books have solution manuals, have good geometric descriptions of the concepts, and are self-contained. The books "Convex Optimization Theory" (2009), "Convex Optimization Algorithms" (2015), and "Convex Analysis & Optimization" (2003) in particular might be a good place to start. ### ⭐⭐⭐⭐⭐ The way to go for introducing optimization *by S***S on May 30, 2008* 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. ### ⭐⭐⭐⭐⭐ Looks great as a monitor stand *by A***Y on March 22, 2026* Looks great as a monitor stand ## Frequently Bought Together - Convex Optimization - Numerical Optimization (Springer Series in Operations Research and Financial Engineering) - Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series) --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.at/products/2966404-convex-optimization](https://www.desertcart.at/products/2966404-convex-optimization) --- *Product available on Desertcart Austria* *Store origin: AT* *Last updated: 2026-04-22*