Stanford EE364A - Convex Optimization I - Boyd
Stephen Boyd

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