Stanford EE364A - Convex Optimization I - Boyd
Stephen Boyd

Stanford-EE364A-ConvexOptimizationI-Boyd (41 files)
lectures/Lecture 1 _ Convex Optimization I (Stanford)-McLq1hEq3UY.mp4220.43MB
lectures/Lecture 2 _ Convex Optimization I (Stanford)-P3W_wFZ2kUo.mp4221.55MB
lectures/Lecture 3 _ Convex Optimization I (Stanford)-kcOodzDGV4c.mp4212.16MB
lectures/Lecture 4 _ Convex Optimization I (Stanford)-lEN2xvTTr0E.mp4201.78MB
lectures/Lecture 5 _ Convex Optimization I (Stanford)-Ry5i8DGZrJs.mp4267.03MB
lectures/Lecture 6 _ Convex Optimization I (Stanford)--T9cloGG_80.mp4189.37MB
lectures/Lecture 7 _ Convex Optimization I-VxQ8VHm1Ci4.mp4250.32MB
lectures/Lecture 8 _ Convex Optimization I (Stanford)-FJVmflArCXc.mp4208.74MB
lectures/Lecture 9 _ Convex Optimization I (Stanford)-3Q9mMluX3Gw.mp4208.89MB
lectures/Lecture 10 _ Convex Optimization I (Stanford)-gH13lxieYFU.mp4212.46MB
lectures/Lecture 11 _ Convex Optimization I (Stanford)-GxK04B9SVg4.mp4258.62MB
lectures/Lecture 12 _ Convex Optimization I (Stanford)-mNzu42FrlHo.mp4354.99MB
lectures/Lecture 13 _ Convex Optimization I (Stanford)-FkPLteYMK40.mp4216.83MB
lectures/Lecture 14 _ Convex Optimization I (Stanford)-ZmvQ7GQ_gPg.mp4191.39MB
lectures/Lecture 15 _ Convex Optimization I (Stanford)-sTCtkkqrY8A.mp4209.23MB
lectures/Lecture 16 _ Convex Optimization I (Stanford)-Ap8LGbCVx4I.mp4243.10MB
lectures/Lecture 17 _ Convex Optimization I (Stanford)-StlHUwd_AgM.mp4259.41MB
lectures/Lecture 18 _ Convex Optimization I (Stanford)-oMRVDILkpUI.mp4318.69MB
lectures/Lecture 19 _ Convex Optimization I (Stanford)-HZW-9Ar0iVc.mp4209.00MB
slides/approx.pdf349.43kB
slides/barrier.pdf190.29kB
slides/chance_constr.pdf90.33kB
slides/conclusions.pdf28.40kB
slides/convexjl_tutorial.pdf80.30kB
slides/cvx_lecture_slides.pdf96.88kB
slides/cvx_tutorial.pdf131.02kB
slides/duality.pdf122.05kB
slides/equality.pdf100.63kB
slides/examples.pdf138.51kB
slides/filters.pdf158.14kB
slides/functions.pdf167.78kB
slides/geom.pdf233.19kB
slides/intro.pdf63.78kB
slides/l1_ext_slides.pdf907.49kB
slides/l1_slides.pdf185.07kB
slides/num-lin-alg.pdf75.41kB
slides/problems.pdf218.36kB
slides/sets.pdf148.28kB
slides/stat.pdf111.33kB
slides/stoch_prog.pdf87.29kB
slides/unconstrained.pdf362.81kB
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= {}
}


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