SDPLR is an ANSI C package developed S. Burer, C. Choi and R.D.C. Monteiro for solving general semidefinite programs (SDPs) using a nonlinear, first-order algorithm that is based on the idea of low-rank factorization. A specialized version of SDPLR is also available for solving specially structured semidefinite programs (SDPs) such as the MaxCut SDP, the Minimum Bisection SDP, and the (unweighted) Lovasz Theta SDP. The details of the algorithm used by SDPLR can be found in the technical report ”A Nonlinear Programming Algorithm for Semidefinite Programs via Low-rank Factorization” written by S. Burer and R.D.C. Monteiro.

References in zbMATH (referenced in 119 articles , 1 standard article )

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  1. Burer, Samuel; Ye, Yinyu: Exact semidefinite formulations for a class of (random and non-random) nonconvex quadratic programs (2020)
  2. Carmon, Yair; Duchi, John C.; Hinder, Oliver; Sidford, Aaron: Lower bounds for finding stationary points I (2020)
  3. Chen, Yuansi; Taeb, Armeen; Bühlmann, Peter: A look at robustness and stability of (\ell_1)-versus (\ell_0)-regularization: discussion of papers by Bertsimas et al. and Hastie et al. (2020)
  4. Chen, Yuxin; Chi, Yuejie; Fan, Jianqing; Ma, Cong; Yan, Yuling: Noisy matrix completion: understanding statistical guarantees for convex relaxation via nonconvex optimization (2020)
  5. Chrétien, Stéphane; Clarkson, Paul: A fast algorithm for the semi-definite relaxation of the state estimation problem in power grids (2020)
  6. Després, Bruno; Herda, Maxime: Computation of sum of squares polynomials from data points (2020)
  7. Duan, Yaqi; Wang, Mengdi; Wen, Zaiwen; Yuan, Yaxiang: Adaptive low-nonnegative-rank approximation for state aggregation of Markov chains (2020)
  8. Eftekhari, Armin; Hauser, Raphael A.: Principal component analysis by optimization of symmetric functions has no spurious local optima (2020)
  9. Gao, Wenbo; Goldfarb, Donald; Curtis, Frank E.: ADMM for multiaffine constrained optimization (2020)
  10. Ha, Wooseok; Liu, Haoyang; Barber, Rina Foygel: An equivalence between critical points for rank constraints versus low-rank factorizations (2020)
  11. Hu, Jiang; Liu, Xin; Wen, Zai-Wen; Yuan, Ya-Xiang: A brief introduction to manifold optimization (2020)
  12. Li, Xiao; Zhu, Zhihui; Man-Cho So, Anthony; Vidal, René: Nonconvex robust low-rank matrix recovery (2020)
  13. Li, Xinrong; Xiu, Naihua; Zhou, Shenglong: Matrix optimization over low-rank spectral sets: stationary points and local and global minimizers (2020)
  14. Waldspurger, Irène; Waters, Alden: Rank optimality for the Burer-Monteiro factorization (2020)
  15. Yu, Ming; Gupta, Varun; Kolar, Mladen: Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach (2020)
  16. Buchheim, Christoph; Montenegro, Maribel; Wiegele, Angelika: SDP-based branch-and-bound for non-convex quadratic integer optimization (2019)
  17. Campos, Juan S.; Misener, Ruth; Parpas, Panos: A multilevel analysis of the Lasserre hierarchy (2019)
  18. Ling, Shuyang; Xu, Ruitu; Bandeira, Afonso S.: On the landscape of synchronization networks: a perspective from nonconvex optimization (2019)
  19. Nayak, Rupaj Kumar; Mohanty, Nirmalya Kumar: Improved row-by-row method for binary quadratic optimization problems (2019)
  20. Amini, Arash A.; Levina, Elizaveta: On semidefinite relaxations for the block model (2018)

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