Arnesh Sujanani

Jacob Aguirre, Diego Cifuentes, Vincent Guigues et al.

We present a Julia-based interface to the precompiled HALLaR and cuHALLaR binaries for large-scale semidefinite programs (SDPs). Both solvers are established as fast and numerically stable, and accept problem data in formats compatible with SDPA and a new enhanced data format taking advantage of Hybrid Sparse Low-Rank (HSLR) structure. The interface allows users to load custom data files, configure solver options, and execute experiments directly from Julia. A collection of example problems is included, including the SDP relaxations of the Matrix Completion and Maximum Stable Set problems.

Saeed Ghadimi, Woosuk L. Jung, Arnesh Sujanani et al.

Preconditioning (scaling) is essential in many areas of mathematics, and in particular in optimization. In this work, we study the problem of finding an optimal diagonal preconditioner. We focus on minimizing two different notions of condition number: the classical, worst-case type, $κ$-condition number, and the more averaging motivated $ω$-condition number. We provide affine based pseudoconvex reformulations of both optimization problems. The advantage of our formulations is that the gradient of the objective is inexpensive to compute and the optimization variable is just an $n\times 1$ vector. We also provide elegant characterizations of the optimality conditions of both problems. We develop a competitive subgradient method, with convergence guarantees, for $κ$-optimal diagonal preconditioning that scales much better and is more efficient than existing SDP-based approaches. We also show that the preconditioners found by our subgradient method leads to better PCG performance for solving linear systems than other approaches. Finally, we show the interesting phenomenon that we can apply the $ω$-optimal preconditioner to the exact $κ$-optimally diagonally preconditioned matrix $A$ and get consistent, significantly improved convergence results for PCG methods.