Extensions of Fast-Lipschitz Optimization for Convex and Non-convex Problems*

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Extensions of Fast-Lipschitz Optimization for Convex and Non-convex Problems ?

Fast-Lipschitz optimization has been recently proposed as a new framework with numerous computational advantages for both centralized and decentralized convex and nonconvex optimization problems. Such a framework generalizes the interference function optimization, which plays an essential role distributed radio power optimization over wireless networks. The characteristics of Fast-Lipschitz met...

متن کامل

Convex Optimization For Non-Convex Problems via Column Generation

We apply column generation to approximating complex structured objects via a set of primitive structured objects under either the cross entropy or L2 loss. We use L1 regularization to encourage the use of few structured primitive objects. We attack approximation using convex optimization over an infinite number of variables each corresponding to a primitive structured object that are generated ...

متن کامل

Asynchronous Non-Convex Optimization for Separable Problems

This paper considers the distributed optimization of a sum of locally observable, nonconvex functions. The optimization is performed over a multi-agent networked system, and each local function depends only on a subset of the variables. An asynchronous and distributed alternating directions method of multipliers (ADMM) method that allows the nodes to defer or skip the computation and transmissi...

متن کامل

Lock-Free Optimization for Non-Convex Problems

Stochastic gradient descent (SGD) and its variants have attracted much attention in machine learning due to their efficiency and effectiveness for optimization. To handle largescale problems, researchers have recently proposed several lock-free strategy based parallel SGD (LF-PSGD) methods for multi-core systems. However, existing works have only proved the convergence of these LF-PSGD methods ...

متن کامل

An algorithm for approximating nondominated points of convex multiobjective optimization problems

‎In this paper‎, ‎we present an algorithm for generating approximate nondominated points of a multiobjective optimization problem (MOP)‎, ‎where the constraints and the objective functions are convex‎. ‎We provide outer and inner approximations of nondominated points and prove that inner approximations provide a set of approximate weakly nondominated points‎. ‎The proposed algorithm can be appl...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: IFAC Proceedings Volumes

سال: 2012

ISSN: 1474-6670

DOI: 10.3182/20120914-2-us-4030.00056