6 papers · 1 filter
Solving Sparsity Constrained PCA, Regression, and QCQP via the Spartrahedron
Diego Cifuentes, Zhuorui Li
Sparsity is a fundamental modeling principle in statistics, signal processing, and data science. However, optimization with sparsity constraints is notoriously difficult. We introd…
Solving exact and noisy rank-one tensor completion with semidefinite programming
Diego Cifuentes, Zhuorui Li
Consider recovering a rank-one tensor of size from exact or noisy observations of a few of its entries. We tackle this problem via semidefinite progr…
cuHALLaR: A GPU Accelerated Low-Rank Augmented Lagrangian Method for Large-Scale Semidefinite Programming
Jacob M. Aguirre, Diego Cifuentes, Vincent Guigues +3
This paper introduces cuHALLaR, a GPU-accelerated implementation of the HALLaR method proposed in Monteiro et al. 2024 for solving large-scale semidefinite programming (SDP) proble…
A User Manual for cuHALLaR: A GPU Accelerated Low-Rank Semidefinite Programming Solver
Jacob Aguirre, Diego Cifuentes, Vincent Guigues +3
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 numericall…
Discrete Shortest Paths in Optimal Power Flow Feasible Regions
Daniel Turizo, Diego Cifuentes, Anton Leykin +1
Optimal power flow (OPF) is a critical optimization problem for power systems to operate at points where cost or other operational objectives are optimized. Due to the non-convexit…
A low-rank augmented Lagrangian method for large-scale semidefinite programming based on a hybrid convex-nonconvex approach
Renato D. C. Monteiro, Arnesh Sujanani, Diego Cifuentes
This paper introduces HALLaR, a new first-order method for solving large-scale semidefinite programs (SDPs) with bounded domain. HALLaR is an inexact augmented Lagrangian (AL) meth…