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Steve Tjoa -- Signal Processing, Machine Learning, Music Information Retrieval. Ph.D., Electrical Engineering, University of Maryland. Lives in San Francisco, CA, USA.

This is the blog used for the course Sounds of Stanford, part of the Arts Intensive Program of Stanford University, that took place from August 31st to September 16th 2009 at CCRMA/Stanford University. Teachers: Xavier Serra (xjs at ccrma.stanford.edu) and Jay Kadis (jay at ccrma.stanford.edu) Teaching Assistant: Jakes Bejoy (jakesb at ccrma.stanford.edu) Students: Lucy Bailey,…

A large community edited collection of Mathematica packages. It is the perfect place to share and find Mathematica packages.

Optimization is the science of making a best choice in the face of conflicting requirements. Any convex optimization problem has geometric interpretation. If a given optimization problem can be transformed to a convex equivalent, then this interpretive benefit is acquired. That is a powerful attraction: the ability to visualize geometry of an optimization problem. Conversely, recent advances in geometry hold convex optimization within their proofs' core. This book is about convex optimization, convex geometry (with particular attention to distance geometry), geometrical problems, and problems that can be transformed into geometrical problems. Euclidean distance geometry is, fundamentally, a determination of point conformation from interpoint distance information; e.g., given only distance information, determine whether there corresponds a realizable configuration of points; a list of points in some dimension that attains the given interpoint distances., Expert information on convex optimization expert calculus of inequalities

Social Sound Design