Linear Programming

The Role of Level-Set Geometry on the Performance of PDHG for Conic Linear Optimization

  • Zikai Xiong
  • Robert M. Freund
    • Categories Convex Optimization, Linear Programming, Linear, Cone and Semidefinite Programming Tags , , ,

    We consider solving huge-scale instances of (convex) conic linear optimization problems, at the scale where matrix-factorization-free methods are attractive or necessary. The restarted primal-dual hybrid gradient method (rPDHG) — with heuristic enhancements and GPU implementation — has been very successful in solving huge-scale linear programming (LP) problems; however its application to more general conic convex … Read more

    On the integrality gap of the Complete Metric Steiner Tree Problem via a novel formulation

  • Ambrogio Maria Bernardelli
  • Eleonora Vercesi
  • Stefano Gualandi
  • Monaldo Mastrolilli
  • Luca Maria Gambardella
    • Categories Combinatorial Optimization, Graphs and Matroids, Linear Programming Tags , ,

    In this work, we compute the lower bound of the integrality gap of the Metric Steiner Tree Problem (MSTP) on a graph for some small values of number of nodes and terminals. After debating about some limitations of the most used formulation for the Steiner Tree Problem, namely the Bidirected Cut Formulation, we introduce a … Read more

    Counterfactual Explanations for Linear Optimization

  • Jannis Kurtz
  • Ş. İlker Birbil
  • Dick den Hertog
    • Categories Applications - OR and Management Sciences, Linear Programming Tags , , ,

    The concept of counterfactual explanations (CE) has emerged as one of the important concepts to understand the inner workings of complex AI systems. In this paper, we translate the idea of CEs to linear optimization and propose, motivate, and analyze three different types of CEs: strong, weak, and relative. While deriving strong and weak CEs … Read more

    Sensitivity Analysis in Dantzig-Wolfe Decomposition

  • Kouhei Harada
    • Categories Linear Programming Tags , , ,

    Dantzig-Wolfe decomposition is a well-known classical method for solving huge linear optimization problems with a block-angular structure. The most computationally expensive process in the method is pricing: solving block subproblems for a dual variable to produce new columns. Therefore, when we want to solve a slightly perturbated problem in which the block-angular structure is preserved … Read more

    An inexact infeasible arc-search interior-point method for linear programming problems

  • Einosuke Iida
  • Makoto Yamashita
    • Categories Linear Programming, Linear, Cone and Semidefinite Programming

    Inexact interior-point methods (IPMs) are a type of interior-point methods that inexactly solve the linear equation system for obtaining the search direction. On the other hand, arc-search IPMs approximate the central path with an ellipsoidal arc obtained by solving two linear equation systems in each iteration, while conventional line-search IPMs solve one linear system, therefore, … Read more

    A Primal-Dual Frank-Wolfe Algorithm for Linear Programming

  • Matthew Hough
  • Stephen A. Vavasis
    • Categories Linear Programming Tags , , ,

    \(\) We present two first-order primal-dual algorithms for solving saddle point formulations of linear programs, namely FWLP (Frank-Wolfe Linear Programming) and FWLP-P. The former iteratively applies the Frank-Wolfe algorithm to both the primal and dual of the saddle point formulation of a standard-form LP. The latter is a modification of FWLP in which regularizing perturbations … Read more

    Exploiting Symmetries in Optimal Quantum Circuit Design

  • Frank de Meijer
  • Dion C. Gijswijt
  • Renata Sotirov
    • Categories Applications - Science and Engineering, Graphs and Matroids, Linear Programming Tags , , , , ,

    A physical limitation in quantum circuit design is the fact that gates in a quantum system can only act on qubits that are physically adjacent in the architecture. To overcome this problem, SWAP gates need to be inserted to make the circuit physically realizable. The nearest neighbour compliance problem (NNCP) asks for an optimal embedding … Read more

    Computational Guarantees for Restarted PDHG for LP based on “Limiting Error Ratios” and LP Sharpness

  • Zikai Xiong
  • Robert M. Freund
    • Categories Convex Optimization, Linear Programming, Linear, Cone and Semidefinite Programming Tags , , , , , , ,

    In recent years, there has been growing interest in solving linear optimization problems – or more simply “LP” – using first-order methods in order to avoid the costly matrix factorizations of traditional methods for huge-scale LP instances. The restarted primal-dual hybrid gradient method (PDHG) – together with some heuristic techniques – has emerged as a … Read more

    On the Relation Between LP Sharpness and Limiting Error Ratio and Complexity Implications for Restarted PDHG

  • Zikai Xiong
  • Robert M. Freund
    • Categories Convex Optimization, Linear Programming, Linear, Cone and Semidefinite Programming Tags , , , , , , ,

    There has been a recent surge in development of first-order methods (FOMs) for solving huge-scale linear programming (LP) problems. The attractiveness of FOMs for LP stems in part from the fact that they avoid costly matrix factorization computation. However, the efficiency of FOMs is significantly influenced – both in theory and in practice – by … Read more

    Distributionally robust optimization through the lens of submodularity

  • Karthik Natarajan
  • Divya Padmanabhan
  • Arjun Ramachandra
    • Categories Convex Optimization, Linear Programming, Linear, Cone and Semidefinite Programming Tags , , , ,

    Distributionally robust optimization is used to solve decision making problems under adversarial uncertainty where the distribution of the uncertainty is itself ambiguous. In this paper, we identify a class of these instances that is solvable in polynomial time by viewing it through the lens of submodularity. We show that the sharpest upper bound on the … Read more