alternating direction method of multipliers for the extended trust region subproblem

نویسندگان

maziar salahi

akram taati

چکیده

the extended trust region subproblem has been the focus of several research recently. under various assumptions, strong duality and certain socp/sdp relaxations have been proposed for several classes of it. due to its importance, in this paper, without any assumption on the problem, we apply the widely used alternating direction method of multipliers (admm) to solve it. the convergence of admm iterations to the first order stationary conditions is established. on several classes of test problems, the quality of the solution obtained by the admm for medium scale problems is compared with the socp/sdp relaxation. moreover, the applicability of the method for solving large scale problems is shown by solving several large instances.

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

On SOCP/SDP Formulation of the Extended Trust Region Subproblem

We consider the extended trust region subproblem (eTRS) as the minimization of an indefinite quadratic function subject to the intersection of unit ball with a single linear inequality constraint. Using a variation of the S-Lemma, we derive the necessary and sufficient optimality conditions for eTRS. Then, an OCP/SDP formulation is introduced for the problem. Finally, several illustrative examp...

متن کامل

Bregman Alternating Direction Method of Multipliers

The mirror descent algorithm (MDA) generalizes gradient descent by using a Bregman divergence to replace squared Euclidean distance. In this paper, we similarly generalize the alternating direction method of multipliers (ADMM) to Bregman ADMM (BADMM), which allows the choice of different Bregman divergences to exploit the structure of problems. BADMM provides a unified framework for ADMM and it...

متن کامل

Adaptive Stochastic Alternating Direction Method of Multipliers

The Alternating Direction Method of Multipliers (ADMM) has been studied for years. Traditional ADMM algorithms need to compute, at each iteration, an (empirical) expected loss function on all training examples, resulting in a computational complexity proportional to the number of training examples. To reduce the complexity, stochastic ADMM algorithms were proposed to replace the expected loss f...

متن کامل

Fast Stochastic Alternating Direction Method of Multipliers

In this paper, we propose a new stochastic alternating direction method of multipliers (ADMM) algorithm, which incrementally approximates the full gradient in the linearized ADMM formulation. Besides having a low per-iteration complexity as existing stochastic ADMM algorithms, the proposed algorithm improves the convergence rate on convex problems from O ( 1 √ T ) to O ( 1 T ) , where T is the ...

متن کامل

An inertial alternating direction method of multipliers

In the context of convex optimization problems in Hilbert spaces, we induce inertial effects into the classical ADMM numerical scheme and obtain in this way so-called inertial ADMM algorithms, the convergence properties of which we investigate into detail. To this aim we make use of the inertial version of the DouglasRachford splitting method for monotone inclusion problems recently introduced ...

متن کامل

Stochastic Alternating Direction Method of Multipliers

The Alternating Direction Method of Multipliers (ADMM) has received lots of attention recently due to the tremendous demand from large-scale and data-distributed machine learning applications. In this paper, we present a stochastic setting for optimization problems with non-smooth composite objective functions. To solve this problem, we propose a stochastic ADMM algorithm. Our algorithm applies...

متن کامل

منابع من

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


عنوان ژورنال:
iranian journal of numerical analysis and optimization

جلد ۷، شماره ۱، صفحات ۱۰۷-۰

کلمات کلیدی

میزبانی شده توسط پلتفرم ابری doprax.com

copyright © 2015-2023