Neuware - This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems. Book Description Springer Verlag, Condition: Brand New. In Stock.
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Monday, January 25, - pm - pm. The performance of each CPU core stopped improving around The Moore's law, however, continues to apply -- not to single-thread performance -- but the number of cores in each computer. Today, workstations are with 64 cores, graphic cards with thousands of GPU cores, and some cellphones with eight cores are sold at affordable prices.
This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm. of convex optimization theory and applications. The selection of topics is signiﬁcantly inﬂuenced by the valuable textbook. Convex Optimization.
To benefit from this multi-core Moore's law, we must parallelize our algorithms. I will present a new method for unconstrained optimization of a smooth and strongly convex function, which attains the optimal rate of convergence of Nesterov's accelerated gradient descent.
The new algorithm has a simple geometric interpretation, loosely inspired by the ellipsoid method. In practice the new method seems to be superior to Nesterov's accelerated gradient descent. Tuesday, January 26, - pm - pm. State statistics of linear systems satisfy certain structural constraints that arise from the underlying dynamics and the directionality of input disturbances. In this talk, we study the problem of completing partially known state statistics. Our aim is to develop tools that can be used in the context of control-oriented modeling of large-scale dynamical systems.
For the type of applications we have in mind, the dynamical interaction between state variables is known while the directionality and dynamics of input excitation is often uncertain. Introduction to dynamical systems. Linear dynamics, stability, Lyapunov functions, eigenvalue conditions. Tuesday, May 27, - am - am. Pablo Parrilo Massachusetts Institute of Technology.