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• CVXOPT is a free software package for convex optimization based on the Python programming language.
• Convex optimization studies the problem of minimizing a convex function over a convex set.
• Surprisingly, algorithms for convex optimization have also been used to design counting problems over discrete objects such as matroids.
• Simultaneously, algorithms for convex optimization have become central to many modern machine learning applications.
• The goal of this book is to enable a reader to gain an in depth understanding of algorithms for convex optimization.
• Convex optimization has many applications ranging from operations research and machine learning to quantum information theory.
• The goal of this course is to investigate in-depth and to develop expert knowledge in the theory and algorithms for convex optimization.
• The lecture slides are adopted from Dr. Stephen Boyd's letcture notes on Convex Optimization at Standord University.
• x + &bgr; y ) = &agr; f i( x ) + &bgr; f i( y )), the problem is said to be one of convex optimization.
• Note that linear programming is a special case of convex optimization, where the objective and constraint functions are all linear.
• If a problem can be transformed to an equivalent convex optimization, then ability to visualize its geometry is acquired.
• Study of equivalence, sameness, and uniqueness therefore pervade study of convex optimization.
• A MOOC on convex optimization, CVX101, was run from 1/21/14 to 3/14/14.
• Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets.
• Extensions of convex optimization include the optimization of biconvex, pseudo-convex, and quasiconvex functions.
• This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms.
• A wealth of existing methodology for convex optimization can then be used to identify points arbitrarily close to the true global optimum.

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• ID : Q463359