Distributed Convex Optimization with Many Convex Constraints
نویسندگان
چکیده
We address the problem of solving convex optimization problems with many convex constraints in a distributed setting. Our approach is based on an extension of the alternating direction method of multipliers (ADMM) that recently gained a lot of attention in the Big Data context. Although it has been invented decades ago, ADMM so far can be applied only to unconstrained problems and problems with linear equality or inequality constraints. Our extension can handle arbitrary inequality constraints directly. It combines the ability of ADMM to solve convex optimization problems in a distributed setting with the ability of the Augmented Lagrangian method to solve constrained optimization problems, and as we show, it inherits the convergence guarantees of ADMM and the Augmented Lagrangian method.
منابع مشابه
Two-Level Optimization Problems with Infinite Number of Convex Lower Level Constraints
This paper proposes a new form of optimization problem which is a two-level programming problem with infinitely many lower level constraints. Firstly, we consider some lower level constraint qualifications (CQs) for this problem. Then, under these CQs, we derive formula for estimating the subdifferential of its valued function. Finally, we present some necessary optimality condit...
متن کاملCharacterization of Properly Efficient Solutions for Convex Multiobjective Programming with Nondifferentiable vanishing constraints
This paper studies the convex multiobjective optimization problem with vanishing constraints. We introduce a new constraint qualification for these problems, and then a necessary optimality condition for properly efficient solutions is presented. Finally by imposing some assumptions, we show that our necessary condition is also sufficient for proper efficiency. Our results are formula...
متن کاملConvex Surface Visualization Using Rational Bi- cubic Function
The rational cubic function with three parameters has been extended to rational bi-cubic function to visualize the shape of regular convex surface data. The rational bi-cubic function involves six parameters in each rectangular patch. Data dependent constraints are derived on four of these parameters to visualize the shape of convex surface data while other two are free to refine the shape of s...
متن کاملLinear Objective Function Optimization with the Max-product Fuzzy Relation Inequality Constraints
In this paper, an optimization problem with a linear objective function subject to a consistent finite system of fuzzy relation inequalities using the max-product composition is studied. Since its feasible domain is non-convex, traditional linear programming methods cannot be applied to solve it. We study this problem and capture some special characteristics of its feasible domain and optimal s...
متن کاملAn 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 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:
- CoRR
دوره abs/1610.02967 شماره
صفحات -
تاریخ انتشار 2016