We study the problem of detecting infeasibility of large-scale linear programming problems using the primal-dual hybrid gradient method (PDHG) of Chambolle and Pock (2011). The literature on PDHG has mostly focused on settings where the problem at hand is assumed to be feasible. When the problem is not feasible, the iterates of the algorithm do … Read more
We provide a tutorial-type review on stochastic dual dynamic programming (SDDP), as one of the state-of-the-art solution methods for large-scale multistage stochastic programs. Since introduced about 30 years ago for solving large-scale multistage stochastic linear programming problems in energy planning, SDDP has been applied to practical problems from several fields and is enriched by various … Read more
Many practical applications require the solution of numerically challenging linear programs (LPs) and mixed-integer programs (MIPs). Scaling is a widely used preconditioning technique that aims at reducing the error propagation of the involved linear systems, thereby improving the numerical behavior of the dual simplex algorithm and, consequently, LP-based branch-and-bound. A reliable scaling method often makes … Read more
The dual face algorithm for linear programming (LP) was proposed by the author in 2014. Using QR factorization, it proceeds from dual face to dual face, until reaching an optimal dual face along with dual and primal optimal solutions, unless detecting infeasibility of the problem. On the other hand, a variant of the algorithm using … Read more
We give simple iterative methods for computing approximately optimal primal and dual solutions for the problem of maximizing a linear functional over a convex set $K$ given by a separation oracle. In contrast to prior work, our algorithms directly output primal and dual solutions and avoid a common requirement of binary search on the objective … Read more
The unique feature of the face algorithm \cite{pan14} is that it moves from face to face, rather than from vertex to vertex as the simplex algorithm. It uses the orthogonal projection of the negative objective gradient on the related null space as its search direction. Nevertheless, the algorithm is based on QR factorization, which would … Read more
In 2011, Korovin and Voronkov (Proceedings of the 23rd International Conference on Automated Deduction, vol. 6803 of Lecture Notes in Computer Science, pp. 369-383) proposed a method based on bound propagation for solving systems of linear inequalities. In this paper, an alternate description of their algorithm which also incorporates an addition that returns a certificate … Read more
The problem of community detection with two equal-sized communities is closely related to the minimum graph bisection problem over certain random graph models. In the stochastic block model distribution over networks with community structure, a well-known semidefinite programming (SDP) relaxation of the minimum bisection problem recovers the underlying communities whenever possible. Motivated by their superior … Read more
This paper focuses on the parametric analysis of a conic linear optimization problem with respect to the perturbation of the objective function along many fixed directions. We introduce the concept of the primal and dual conic linear inequality representable sets, which is very helpful for converting the correlation of the parametric conic linear optimization problems … Read more
Community detection is a widely-studied unsupervised learning problem in which the task is to group similar entities together based on observed pairwise entity interactions. This problem has applications in diverse domains such is social network analysis and computational biology. There is a significant amount of literature studying this problem under the assumption that the communities … Read more