Stanford EE364A - Convex Optimization I - Boyd
Stephen Boyd

Stanford-EE364A-ConvexOptimizationI-Boyd (41 files)
lectures/Lecture 1 _ Convex Optimization I (Stanford)-McLq1hEq3UY.mp4 220.43MB
lectures/Lecture 2 _ Convex Optimization I (Stanford)-P3W_wFZ2kUo.mp4 221.55MB
lectures/Lecture 3 _ Convex Optimization I (Stanford)-kcOodzDGV4c.mp4 212.16MB
lectures/Lecture 4 _ Convex Optimization I (Stanford)-lEN2xvTTr0E.mp4 201.78MB
lectures/Lecture 5 _ Convex Optimization I (Stanford)-Ry5i8DGZrJs.mp4 267.03MB
lectures/Lecture 6 _ Convex Optimization I (Stanford)--T9cloGG_80.mp4 189.37MB
lectures/Lecture 7 _ Convex Optimization I-VxQ8VHm1Ci4.mp4 250.32MB
lectures/Lecture 8 _ Convex Optimization I (Stanford)-FJVmflArCXc.mp4 208.74MB
lectures/Lecture 9 _ Convex Optimization I (Stanford)-3Q9mMluX3Gw.mp4 208.89MB
lectures/Lecture 10 _ Convex Optimization I (Stanford)-gH13lxieYFU.mp4 212.46MB
lectures/Lecture 11 _ Convex Optimization I (Stanford)-GxK04B9SVg4.mp4 258.62MB
lectures/Lecture 12 _ Convex Optimization I (Stanford)-mNzu42FrlHo.mp4 354.99MB
lectures/Lecture 13 _ Convex Optimization I (Stanford)-FkPLteYMK40.mp4 216.83MB
lectures/Lecture 14 _ Convex Optimization I (Stanford)-ZmvQ7GQ_gPg.mp4 191.39MB
lectures/Lecture 15 _ Convex Optimization I (Stanford)-sTCtkkqrY8A.mp4 209.23MB
lectures/Lecture 16 _ Convex Optimization I (Stanford)-Ap8LGbCVx4I.mp4 243.10MB
lectures/Lecture 17 _ Convex Optimization I (Stanford)-StlHUwd_AgM.mp4 259.41MB
lectures/Lecture 18 _ Convex Optimization I (Stanford)-oMRVDILkpUI.mp4 318.69MB
lectures/Lecture 19 _ Convex Optimization I (Stanford)-HZW-9Ar0iVc.mp4 209.00MB
slides/approx.pdf 349.43kB
slides/barrier.pdf 190.29kB
slides/chance_constr.pdf 90.33kB
slides/conclusions.pdf 28.40kB
slides/convexjl_tutorial.pdf 80.30kB
slides/cvx_lecture_slides.pdf 96.88kB
slides/cvx_tutorial.pdf 131.02kB
slides/duality.pdf 122.05kB
slides/equality.pdf 100.63kB
slides/examples.pdf 138.51kB
slides/filters.pdf 158.14kB
slides/functions.pdf 167.78kB
slides/geom.pdf 233.19kB
slides/intro.pdf 63.78kB
slides/l1_ext_slides.pdf 907.49kB
slides/l1_slides.pdf 185.07kB
slides/num-lin-alg.pdf 75.41kB
slides/problems.pdf 218.36kB
slides/sets.pdf 148.28kB
slides/stat.pdf 111.33kB
slides/stoch_prog.pdf 87.29kB
slides/unconstrained.pdf 362.81kB
Type: Course
Tags: Optimization, Math

title= {Stanford EE364A - Convex Optimization I - Boyd},
journal= {},
author= {Stephen Boyd},
year= {2008},
url= {},
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

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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