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Best resources to learnConvex Optimization

Study sheet

Convex optimisation solves problems where local optima are global. You will learn convex sets and functions, duality, algorithms and how to model engineering and machine-learning problems as convex programs.

5 resources · · How we curate

🎓Courses(1)

course

CMU 10-725: Convex Optimization (Ryan Tibshirani)

Carnegie Mellon graduate course site with slides, scribed notes, homework and linked lecture videos. It focuses on algorithms used in statistics and machine learning: gradient and subgradient methods, proximal methods, Newton's method, duality, ADMM, coordinate descent and stochastic methods.

advanced🇬🇧

📚Books(2)

bookPaid

First-Order Methods in Optimization

Rigorous reference on first-order algorithms for convex problems: subgradient, proximal gradient, FISTA, mirror descent, smoothing, primal-dual methods, ADMM and conditional gradient, with full convergence proofs and a detailed treatment of proximal operators and conjugate functions.

advanced🇬🇧
bookPaid

Geometric Algorithms and Combinatorial Optimization

Research monograph showing how the ellipsoid method and lattice basis reduction yield polynomial-time algorithms for combinatorial optimization. Establishes the equivalence of separation and optimization over polyhedra, then applies it to matroids, submodular functions, perfect graphs and other structured problems.

advanced🇬🇧

🌐Websites(1)

website

CVXPY Documentation and Tutorials

Official documentation for CVXPY, an open-source Python library for modeling convex problems. It includes tutorials on disciplined convex programming, supported solvers, parameters, and worked examples in machine learning, finance, control and signal processing.

beginner🇬🇧

📄Papers(1)

paper

Convex Optimization: Algorithms and Complexity

Monograph on the complexity theory of convex optimization: ellipsoid and cutting-plane methods, gradient descent in smooth and non-smooth settings, Nesterov acceleration with lower bounds, mirror descent, interior-point methods, and stochastic and randomized algorithms. Published in Foundations and Trends in Machine Learning.

advanced🇬🇧
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Frequently Asked Questions

What are the best free resources to learn Convex Optimization?

Dantes has curated 5 resources for Convex Optimization, including 2 books, 1 courses, 1 websites, 1 papers. All resources are hand-picked for quality — no algorithmic filler. Browse the full list above to find the format that works best for you.

Is Convex Optimization hard to learn?

Convex Optimization is approachable at the beginner level — there are resources here specifically for those starting from scratch. As you progress, intermediate and advanced material is also available to take your skills further.

What types of Convex Optimization learning resources are available on Dantes?

For Convex Optimization, Dantes has curated 2 books, 1 courses, 1 websites, 1 papers. Each resource type serves a different learning style: videos and YouTube for visual learners, books for depth, courses for structured progression, and websites for quick reference.

How does Dantes select Convex Optimization resources?

Dantes is an algorithm-free learning directory. Resources are hand-curated based on quality, accuracy, and usefulness — not engagement metrics or paid placements. The goal is to surface the best learning material for Convex Optimization, whether it's a free YouTube series, a classic textbook, or an open courseware from a top university.

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