Convex optimization
by Stephen P. Boyd · first published 2006
69% recommend29% neutral2% critical
Why HN recommends it
Standard, freely available textbook for learning convex optimization, with companion Stanford EE364a lectures and extra exercises. Builds a versatile math foundation for machine learning, statistics, and modeling.
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…Mathematical Plays (by Berlekamp et al.) - Elements of Information Theory (by Cover & Thomas) - Information Theory, Inference, and Learning Algorithms (by MacKay) - Network Optimization (by Bertsekas) - Convex Optimization (by Boyd & Vandenberghe) - Nonlinear Control Systems (by Isidori) - Visual Complex Analysis (by Needham) - Lasers (by Siegman) - Game Theory (by Fudenberg & Tirole) - Trading and Exchanges (by Harris)…
on “Reading list for those who love to learn” ↗…could actually vouch for would be: CLRS Russell and Norvig Introduction to IR (Manning et al) Database Systems: The Complete Book Others that I've heard good things about are: Dragon Book Convex Optimization (Boyd) Probabilistic Robotics Foundations of Statistical NLP I'd really like a good distributed systems or distributed databases book (rather than reading old Lamport papers or something), as well…
on “Technical Papers Every Programmer Should Read (At Least Twice)” ↗…Wolsey https://books.google.com/books?id=x7RvQgAACAAJ For odd modeling tricks for integer programming, Logic and Integer Programming by Williams, H. Paul https://www.springer.com/us/book/9780387922799 For modeling tricks for convex programming, Convex Optimization by Stephen Boyd and Lieven Vandenberghe https://stanford.edu/~boyd/cvxbook/ For basic nonlinear optimization algorithms, Numerical Optimization by Nocedal, Jorge, Wright, S. https://www.springer.com/us/book/9780387303031…
on “Applied Mathematical Programming (1977)” ↗…Durrett, R. (2005). "Probability: Theory and Examples" Duxbury. Machine learning research also reposes on optimization theory. A good starting book on linear optimization that will prepare you for convex optimization: 8.) Bertsimas, D. and Tsitsiklis, J. (1997). "Introduction to Linear Optimization" Athena. And then you can graduate to: 9.) Boyd, S. and Vandenberghe, L. (2004). "Convex Optimization"…
on “Learning About Statistical Learning” ↗…http://www-stat.stanford.edu/~tibs/ElemStatLearn/ Gaussian Processing for Machine Learning http://www.gaussianprocess.org/gpml/ Bayesian Reasoning and Machine Learning http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online Convex Optimization http://www.stanford.edu/~boyd/cvxbook/
on “Free pdf Books on Machine/Statistical Learning” ↗For theoretical continuous/nonlinear/convex optimization, your #1 is the bible, together with "Convex Optimization" by Boyd & Vandenberghe. However, beware that both are grad textbooks. They can be tough going at times. Unfortunately, I never found undergrad…
on “Ask HN: Literature for mathematical optimization?” ↗Not OP, but probably Convex Optimization [1]. [1] https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf
on “Algebra, Topology, Differential Calculus, and Optimization Theory [pdf]” ↗…(https://smile.amazon.com/gp/product/0387848576( * Stanford CS131 Computer vision (http://vision.stanford.edu/teaching/cs131_fall1617/) * Stanford CS231n Convolutional Neural Networks for Visual Recognition (http://cs231n.github.io/) * Convex Optimization (https://smile.amazon.com/Convex-Optimization-Stephen-Boyd/dp...) * Deep Learning (http://www.deeplearningbook.org/ or https://smile.amazon.com/Deep-Learning-Adaptive-Computation-...) * Neural Networks…
on “Machine Learning for Software Engineers” ↗Introduction to Information Retrieval: http://www.amazon.com/dp/0521865719/ Convex Optimization: http://www.amazon.com/dp/0521833787/ Foundations of Statistical Natural Language Processing: http://www.amazon.com/dp/0262133601/ Read Peter Norvig's review for Foundations of ....http://www.amazon.com/review/R3GSYXSKRU8V17/…
on “Ask HN: After SICP, what next?” ↗…the fundamentals of information theory was amazing. the proofs are easy to follow and explained well so you can match the intuition to formula. i'd also like to recommend Boyd & Vanderberghe's book on Convex Optimization (available here http://www.stanford.edu/~boyd/cvxbook/). This is maybe a little less fun but very deep and still well written and 'illustrated'. My background is in ML and I came to realize over time…
on “Ask HN: good math books” ↗Convex Optimization by Stephen Boyd (Stanford EE364A) available on itunesU. There's also a CVX101 Mooc[1], but I don't how it's different from the original material. IMO it's not the topic itself, but the invaluable material…
on “Ask HN: What's your favorite online course?” ↗Stephen Boyd's Convex Optimization, http://stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf is gorgeous material. Large parts it are from theorems of the alternative, linear programming, Jensen's inequality, R. T. Rockafellar, etc., but Boyd…
on “Foundations Machine Learning” ↗For numerical optimization, a couple good textbooks are: - "Practical Optimization" by P. E. Gill, W. Murray and M. H. Wright: a little old (1982), but provides a solid foundation - "Convex Optimization" by S. Boyd and L. Vandenberghe: the standard for learning convex optimization (also available as a free PDF from the author's website) - "Convex Analysis and Monotone Operator Theory in Hilbert Spaces"…
on “Ask HN: What are the best textbooks in your field of expertise?” ↗…Robotics / Computer Vision: - Thrun et. al, Probabilistic Robotics - Multiple View Geometry, Hartley / Zisserman - An Invitation to 3D Vision, Ma - Pattern Recognition and Machine Learning, Bishop -Convex Optimization, Boyd
on “Ask HN: What are the best textbooks in your field of expertise?” ↗some "out there" suggestions: - "Extremal Combinatorics" by Staysys Jukna - "Convex Optimization" by Boyd and Vandenberghe - "Delay Insensitive Circuits" [1] - "An Introduction to Mathematical Cryptography" by Hoffstein, Pipher, and Silverman - "Quantum Computing since Democritus" by Scott AAronson…
on “Ask HN: Technical Summer Reading List” ↗