Talks on Optimization

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Contents

Slides, Powerpoint, and PDF Presentations  (More talks, in video format)

Sampling Theory and Practice: 50 Ways to Sample your Signal

Martin Vetterli, EPFL & UC Berkeley  (10MByte PowerPoint)

A Feasible Method for Optimization with LaTeX: \|x\|^2\!=1 or LaTeX: \,X^TX\!=I

Zaiwen Wen, Wotao Yin, resp: NSF Postdoc with UCLA and Rice, Department of Computational and Applied Mathematics (CAAM) Rice University, October 2010

Rigidity and Localization: An Optimization Perspective

Anthony Man-Cho So, Dept. of Systems Engineering & Engineering Management, Chinese University of Hong Kong, at Operations Research Seminar, Stanford University, March 15, 2010

Explicit Sensor Network Localization using Semidefinite Programming and Facial Reduction

Nathan Krislock and Henry Wolkowicz, Dept. of Combinatorics and Optimization, University of Waterloo, at ICME Stanford University Friday Oct. 30, 2009

Toward 0-norm Reconstruction, and a Nullspace Technique for Compressive Sampling

Presented by Christine Law with Gary Glover at the Linear Algebra Seminar, University of California, Berkeley, February 4, 2009  (5MByte PowerPoint) Also presented by Christine Law with Gary Glover at the Linear Algebra and Optimization Seminar (CME510), iCME, Stanford University, November 19, 2008

Combining Geometry and Combinatorics: A Unified Approach to Sparse Signal Recovery

Presented by Anna Gilbert at the Applied Mathematics Seminar, Stanford University, October 3, 2008

Compressed Sensing with Contiguous Fourier Measurements

Presented by Jean-François Mercier with Laurent Demanet and George Papanicolaou at the Applied Mathematics Seminar, Stanford University, July 21, 2008

Optimization Problems in Compressed Sensing

by Jalal Fadili, CNRS, ENSI Caen France, at the Applied Mathematics Seminar, Stanford University, July 21, 2008

Compressed Sensing in Astronomy

Presented by Jean-Luc Starck with Jérôme Bobin at the Applied Mathematics Seminar, Stanford University, July 21, 2008

Highly Undersampled 0-Norm Reconstruction

Presented by Christine Law at Lucas Center for Imaging, Stanford University, July 9, 2008  (771KByte PowerPoint)

Nonconvex Compressive Sensing

Presented by Rick Chartrand with Valentina Staneva, Wotao Yin, & Kevin Vixie at the SIAM Conference on Imaging Science 2008, San Diego, California, July 7, 2008

Interior-Point Methods per l’Ottimizzazione Conica: SOCP e SDP

Presented by Andrea Cassioli, Dipartimento di Sistemi e Informatica, Universitá di Firenze, May 28, 2008

Bregman Iterative Algorithms for L1 Minimization with Applications to Compressed Sensing

Presented by Stanley Osher with W. Yin, D. Goldfarb, & J. Darbon at the iCME Colloquium (CME 500), Stanford University, December 3, 2007  (400KByte PowerPoint)

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