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Continuation of Convex Optimization I. Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications. Course requirements include a substantial project.
Prerequisites: Convex Optimization I
Course Homepage: [[http://see.stanford.edu/see/courseinfo.aspx?coll=523bbab2-dcc1-4b5a-b78f-4c9dc8c7cf7a]]
Course features at Stanford Engineering Everywhere page: *Convex Optimization II *Lectures *Syllabus *Handouts *Assignments *Exams *Software
Videos

Lecture 15: Recap: Example: Minimum Cardinality Problem
Jul 21, 2010
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3246 views

Lecture 16: Model Predictive Control
Jul 21, 2010
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6662 views

Lecture 4: Project Subgradient For Dual Problem
Jul 21, 2010
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3005 views

Lecture 18: Announcements
Jul 21, 2010
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2594 views

Lecture 13: Recap: Conjugate Gradient Method
Jul 21, 2010
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3868 views

Lecture 1: Course Logistics
Jul 21, 2010
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5350 views

Lecture 17: Stochastic Model Predictive Control
Jul 21, 2010
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4066 views

Lecture 8: Recap: Ellipsoid Method
Jul 21, 2010
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3062 views

Lecture 3: Convergence Proof
Jul 21, 2010
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2566 views

Lecture 14: Methods (Truncated Newton Method)
Jul 21, 2010
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2723 views

Lecture 5: Stochastic Programming
Jul 21, 2010
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6637 views

Lecture 6: Addendum: Hit-And-Run CG Algorithm
Jul 21, 2010
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2739 views

Lecture 11: Sequential Convex Programming
Jul 21, 2010
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3594 views

Lecture 2: Recap: Subgradients
Jul 21, 2010
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3480 views

Lecture 12: Recap: 'Difference Of Convex' Programming
Jul 21, 2010
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3860 views

Lecture 9: Comments: Latex Typesetting Style
Jul 21, 2010
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4665 views

Lecture 7: Example: Piecewise Linear Minimization
Jul 21, 2010
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3491 views

Lecture 10: Decomposition Applications
Jul 21, 2010
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2834 views