SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0)
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Abstract (arXiv)
SDPNAL+ is a sc Matlab software package that implements an augmented Lagrangian based method to solve large scale semidefinite programming problems with bound constraints. The implementation was initially based on a majorized semismooth Newton-CG augmented Lagrangian method, here we designed it within an inexact symmetric Gauss-Seidel based semi-proximal ADMM/ALM (alternating direction method of multipliers/augmented Lagrangian method) framework for the purpose of deriving simpler stopping conditions and closing the gap between the practical implementation of the algorithm and the theoretical algorithm. The basic code is written in sc Matlab, but some subroutines in C language are incorporated via Mex files. We also design a convenient interface for users to input their SDP models into the solver. Numerous problems arising from combinatorial optimization and binary integer quadratic programming problems have been tested to evaluate the performance of the solver. Extensive numerical experiments conducted in [Yang, Sun, and Toh, Mathematical Programming Computation, 7 (2015), pp. 331--366] show that the proposed method is quite efficient and robust, in that it is able to solve 98.9% of the 745 test instances of SDP problems arising from various applications to the accuracy of 10-6 in the relative KKT residual.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Oct 2017.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 1; 2025: 3.
9 papers.
- 2021An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clusteringcited by 12
- 2022Global Optimization for Cardinality-constrained Minimum Sum-of-Squares Clustering via Semidefinite Programmingcited by 3
- 2024A Semidefinite Programming-Based Branch-and-Cut Algorithm for Biclusteringcited by 2
- 2022Learning Transition Operators From Sparse Space-Time Samplescited by 1
- 2025Graph-based Clustering Revisited: A Relaxation of Kernel k-Means Perspectivecited by 1
- 2023A Novel Black Box Process Quality Optimization Approach based on Hit Ratecited by 0
- 2023A Novel Correlation-optimized Deep Learning Method for Wind Speed Forecastcited by 0
- 2025Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verificationcited by 0
- 2025Exact and Heuristic Algorithms for Constrained Biclusteringcited by 0
- 2025Graph-based Clustering Revisited: A Relaxation of Kernel k-Means Perspective23 Sep 2025
- 2025Exact and Heuristic Algorithms for Constrained Biclustering7 Aug 2025
- 2025Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification12 Jun 2025
- 2024A Semidefinite Programming-Based Branch-and-Cut Algorithm for Biclustering17 Mar 2024
- 2023A Novel Correlation-optimized Deep Learning Method for Wind Speed Forecast3 Jun 2023
- 2023A Novel Black Box Process Quality Optimization Approach based on Hit Rate31 May 2023
- 2022Learning Transition Operators From Sparse Space-Time Samples1 Dec 2022
- 2022Global Optimization for Cardinality-constrained Minimum Sum-of-Squares Clustering via Semidefinite Programming19 Sep 2022
- 2021An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering30 Nov 2021