Asynchronous gradient algorithms for a class of convex separable network flow problems
نویسنده
چکیده
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Asynchronous gradient algorithms for a class of convex separable network flow problems Didier El Baz
منابع مشابه
Solving Quadratic Multicommodity Problems through an Interior-Point Algorithm
Standard interior-point algorithms usually show a poor performance when applied to multicommodity network flows problems. A recent specialized interior-point algorithm for linear multicommodity network flows overcame this drawback, and was able to efficiently solve large and difficult instances. In this work we perform a computational evaluation of an extension of that specialized algorithm for...
متن کاملAccelerating Asynchronous Algorithms for Convex Optimization by Momentum Compensation
Asynchronous algorithms have attracted much attention recently due to the crucial demands on solving large-scale optimization problems. However, the accelerated versions of asynchronous algorithms are rarely studied. In this paper, we propose the “momentum compensation” technique to accelerate asynchronous algorithms for convex problems. Specifically, we first accelerate the plain Asynchronous ...
متن کاملAn efficient one-layer recurrent neural network for solving a class of nonsmooth optimization problems
Constrained optimization problems have a wide range of applications in science, economics, and engineering. In this paper, a neural network model is proposed to solve a class of nonsmooth constrained optimization problems with a nonsmooth convex objective function subject to nonlinear inequality and affine equality constraints. It is a one-layer non-penalty recurrent neural network based on the...
متن کاملAsynchronous Stochastic Gradient Descent with Variance Reduction for Non-Convex Optimization
We provide the first theoretical analysis on the convergence rate of the asynchronous stochastic variance reduced gradient (SVRG) descent algorithm on nonconvex optimization. Recent studies have shown that the asynchronous stochastic gradient descent (SGD) based algorithms with variance reduction converge with a linear convergent rate on convex problems. However, there is no work to analyze asy...
متن کاملAsynchronous Doubly Stochastic Proximal Optimization with Variance Reduction
In the big data era, both of the sample size and dimension could be huge at the same time. Asynchronous parallel technology was recently proposed to handle the big data. Specifically, asynchronous stochastic (variance reduction) gradient descent algorithms were recently proposed to scale the sample size, and asynchronous stochastic coordinate descent algorithms were proposed to scale the dimens...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Comp. Opt. and Appl.
دوره 5 شماره
صفحات -
تاریخ انتشار 1996