Convex Optimization with Abstract Linear Operators, ICCV 2015 | Stephen P. Boyd, Stanford
We introduce a convex optimization modeling framework that transforms a convex optimization problem expressed in a form natural and convenient for the user into an equivalent cone program in a way that preserves fast linear transforms in the original problem. By representing linear functions in the transformation process not as matrices, but as graphs that encode composition of abstract linear operators, we arrive at a matrix-free cone program, i.e., one whose data matrix is represented by an abstract linear operator and its adjoint. This cone program can then be solved by a matrix-free cone solver. By combining the matrix-free modeling framework and cone solver, we obtain a general method for efficiently solving convex optimization problems involving fast linear transforms
Видео Convex Optimization with Abstract Linear Operators, ICCV 2015 | Stephen P. Boyd, Stanford канала Preserve Knowledge
Видео Convex Optimization with Abstract Linear Operators, ICCV 2015 | Stephen P. Boyd, Stanford канала Preserve Knowledge
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Convex Optimization and Applications - Stephen BoydHow AI Powers Self-Driving Tesla with Elon Musk and Andrej KarpathyHow AI Is Beginning To Surpass Humans | Jürgen SchmidhuberFuture is Now - PanelWhat Is Mathematical Optimization?Financial Engineering Playground: Signal Processing, Robust Estimation, Kalman, OptimizationWhat is a Vector Space? (Abstract Algebra)Carl Sagan's 1994 "Lost" Lecture: The Age of ExplorationTesla AI Andrej Karpathy on Scalability in Autonomous DrivingConvex Optimization: An Overview by Stephen Boyd: The 3rd Wook Hyun Kwon LectureLeveraging the Disruptive Power of Artificial IntelligenceP vs. NP - An IntroductionReal-Time Convex OptimizationStephen Boyd - Molten Salt Reactors in Five Years?Creating Human-level AI: How and When?Optimization Part I - Stephen Boyd - MLSS 2015 TübingenVignesh Ganapathi-Subramanian's PhD Defense - Stanford UniversityDistributed Optimization via Alternating Direction Method of MultipliersSemantic Segmentation using Adversarial Networks, NIPS 2016 | Pauline Luc, Facebook AI Research