Towards a multigrid method for the minimum-cost flow problem
Alessio Quaglino, Rolf Krause

TL;DR
This paper introduces a multigrid approach for the min-cost flow problem that leverages algebraic multigrid methods, demonstrating good scalability on large problems despite less competitiveness on small ones.
Contribution
It proposes a novel multigrid strategy combining interior-point and algebraic multigrid methods for large-scale min-cost flow problems, addressing Jacobian indefiniteness.
Findings
Scales well with problem size and parallel processors
Effective on large-scale problems using black-box algebraic multigrid
Less competitive than combinatorial methods on small problems
Abstract
We present a first step towards a multigrid method for solving the min-cost flow problem. Specifically, we present a strategy that takes advantage of existing black-box fast iterative linear solvers, i.e. algebraic multigrid methods. We show with standard benchmarks that, while less competitive than combinatorial techniques on small problems that run on a single core, our approach scales well with problem size, complexity, and number of processors, allowing for tackling large-scale problems on modern parallel architectures. Our approach is based on combining interior-point with multigrid methods for solving the nonlinear KKT equations via Newton's method. However, the Jacobian matrix arising in the Newton iteration is indefinite and its condition number cannot be expected to be bounded. In fact, the eigenvalues of the Jacobian can both vanish and blow up near the solution, leading to a…
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Taxonomy
TopicsAdvanced Numerical Methods in Computational Mathematics · Parallel Computing and Optimization Techniques · Robotic Path Planning Algorithms
