**Type:**Course

**Tags:**Optimization, Math

**Bibtex:**

@article{, title= {Stanford EE364A - Convex Optimization I - Boyd}, journal= {}, author= {Stephen Boyd}, year= {2008}, url= {https://web.stanford.edu/class/ee364a/}, license= {}, abstract= {Catalog description Concentrates on recognizing and solving convex optimization problems that arise in applications. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance. Course objectives to give students the tools and training to recognize convex optimization problems that arise in applications to present the basic theory of such problems, concentrating on results that are useful in computation to give students a thorough understanding of how such problems are solved, and some experience in solving them to give students the background required to use the methods in their own research work or applications Videos 1. Introduction 2. Convex sets 3. Convex functions 4. Convex optimization problems 5. Duality 6. Approximation and fitting 7. Statistical estimation 8. Geometric problems 9. Numerical linear algebra background 10. Unconstrained minimization 11. Equality constrained minimization 12. Interior-point methods 13. Conclusions }, keywords= {Optimization, Math}, terms= {} }