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20182026
most citedAn Inexact Projected Gradient Method with Rounding and Lifting by Nonlinear Programming for Solving Rank-One Semidefinite Relaxation of Polynomial Optimization

2 citations · 6 across the 18 of their papers we have counts for

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math.OC2021★ 2 cited

An Inexact Projected Gradient Method with Rounding and Lifting by Nonlinear Programming for Solving Rank-One Semidefinite Relaxation of Polynomial Optimization

Heng Yang, Ling Liang, Luca Carlone +1

We consider solving high-order semidefinite programming (SDP) relaxations of nonconvex polynomial optimization problems (POPs) that often admit degenerate rank-one optimal solution…

math.OC2021

QPPAL: A two-phase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems

Ling Liang, Xudong Li, Defeng Sun +1

In this paper, we aim to solve high dimensional convex quadratic programming (QP) problems with a large number of quadratic terms, linear equality and inequality constraints. In or…