In this paper, we show a way to exploit sparsity in the problem data in a primal-dual potential reduction method for solving a class of semidefinite programs. When the problem data is sparse, the dual variable is also sparse, but the primal one is not. To avoid working with the dense primal variable, we apply Fukuda et al.’s theory of partial matrix completion and work with partial matrices ins...

This paper studies how to train machine-learning models that directly approximate the optimal solutions of constrained optimization problems. is an empirical risk minimization under constraints, which challenging as training must balance optimality and feasibility conditions. Supervised learning methods often approach this challenge by model on a large collection pre-solved instances. takes dif...

An algorithm for minimizing a nonlinear function subject to nonlinear inequality constraints is described It applies sequential quadratic programming techniques to a sequence of barrier problems and uses trust regions to ensure the robustness of the iteration and to allow the direct use of second order derivatives This framework permits primal and primal dual steps but the paper focuses on the ...

In this paper, we generalize the concept of sensitivity analysis in fuzzy number linear programming (FLNP) problems by applying fuzzy simplex algorithms and using the general linear ranking functions on fuzzy numbers. The purpose of sensitivity analysis is to determine changes in the optimal solution of FNLP problem resulting from changes in the data. If the change affects the optimality of the...

We consider convex-concave saddle point problems with a separable structure and non-strongly convex functions. We propose an efficient stochastic block coordinate descent method using adaptive primal-dual updates, which enables flexible parallel optimization for large-scale problems. Our method shares the efficiency and flexibility of block coordinate descent methods with the simplicity of prim...

The paper presents a new algorithmic approach for multistage stochastic programs which are seen as discrete optimal control problems with a characteristic dynamic structure induced by the scenario tree. To exploit that structure, we propose a highly eecient dynamic programming recursion for the computationally intensive task of KKT systems solution within a primal-dual interior point method. Co...

Two primal{dual a ne scaling algorithms for linear programming are extended to semide nite programming. The algorithms do not require (nearly) centered starting solutions, and can be initiated with any primal{dual feasible solution. The rst algorithm is the Dikin-type a ne scaling method of Jansen et al. [8] and the second the pure a ne scaling method of Monteiro et al. [12]. The extension of t...

We propose a family of search directions based on primal-dual entropy in the context of interior point methods for linear programming. This new family contains previously proposed search directions in the context of primal-dual entropy. We analyze the new family of search directions by studying their primal-dual affine-scaling and constant-gap centering components. We then design primal-dual in...