mlsanityData to 14 Oct 2026

Scalable Semidefinite Programming

arXiv 1912.02949 ↗First posted 6 Dec 2019math.OC math.COcited by 36

Not on the map. Why?

Abstract (arXiv)

Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This paper develops a provably correct randomized algorithm for solving large, weakly constrained SDP problems by economizing on the storage and arithmetic costs. Numerical evidence shows that the method is effective for a range of applications, including relaxations of MaxCut, abstract phase retrieval, and quadratic assignment. Running on a laptop equivalent, the algorithm can handle SDP instances where the matrix variable has over 1014 entries.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Dec 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 7; 2023: 14; 2024: 3; 2025: 3; 2026: 4.

36 papers.

Sort:

  1. 2023Learning to Warm-Start Fixed-Point Optimization Algorithmscited by 32
  2. 2023Provably Faster Gradient Descent via Long Stepscited by 20
  3. 2023Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergencecited by 11
  4. 2021An Inexact Projected Gradient Method with Rounding and Lifting by Nonlinear Programming for Solving Rank-One Semidefinite Relaxation of Polynomial Optimizationcited by 10
  5. 2022Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimizationcited by 8
  6. 2021Training Quantized Neural Networks to Global Optimality via Semidefinite Programmingcited by 6
  7. 2022Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensingcited by 5
  8. 2023Principal Component Analysis in Space Formscited by 5
  9. 2023Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimizationcited by 4
  10. 2023Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketchingcited by 4
  11. 2021Randomized Algorithms for Scientific Computing (RASC)cited by 3
  12. 2023Higher-Order Newton Methods with Polynomial Work per Iterationcited by 3
24 more
  1. 2021Federated Myopic Community Detection with One-shot Communicationcited by 2
  2. 2022Support Recovery in Sparse PCA with Incomplete Datacited by 2
  3. 2022Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimizationcited by 1
  4. 2022Learning Transition Operators From Sparse Space-Time Samplescited by 1
  5. 2023How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise?cited by 1
  6. 2023Partial Inference in Structured Predictioncited by 1
  7. 2023Sub-universal variational circuits for combinatorial optimization problemscited by 1
  8. 2024Interventional Causal Structure Discovery over Graphical Models with Convergence and Optimality Guaranteescited by 1
  9. 2026Learning to Optimize by Differentiable Programmingcited by 1
  10. 2026Cascading Robustness Verification: Toward Efficient Model-Agnostic Certificationcited by 1
  11. 2021Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPscited by 0
  12. 2022A conditional gradient homotopy method with applications to Semidefinite Programmingcited by 0
  13. 2022A Novel Plug-and-Play Approach for Adversarially Robust Generalizationcited by 0
  14. 2023Support Recovery in Sparse PCA with Non-Random Missing Datacited by 0
  15. 2023Oblivious Stochastic Composite Optimizationcited by 0
  16. 2023Conic Descent Redux for Memory-Efficient Optimizationcited by 0
  17. 2023Locally Stationary Graph Processescited by 0
  18. 2024Suboptimality bounds for trace-bounded SDPs enable a faster and scalable low-rank SDP solver SDPLR+cited by 0
  19. 2024Subspace Langevin Monte Carlocited by 0
  20. 2025Optimistic Online Learning in Symmetric Cone Gamescited by 0
  21. 2025A Smoothing Newton Method for Rank-one Matrix Recoverycited by 0
  22. 2025Simplifying Optimal Transport through Schatten-p Regularizationcited by 0
  23. 2026SATORIS-N: Spectral Analysis based Traffic Observation Recovery via Informed Subspaces and Nuclear-norm minimizationcited by 0
  24. 2026Direction-Magnitude Decomposition for Low-Rank Matrix Optimization: Faster Convergence and Saddle-to-saddle Dynamicscited by 0