Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Optimization Letters

Solving SDP relaxations of Max-Cut problem with large number of hypermetric inequalities by L-BFGS-B

Timotej Hrga, Janez Povh

Published Nov 4, 2022• 3 citations
Publisher
DOI
10.1007/s11590-022-01944-z

Related papers

Solving Max-Cut to optimality by intersecting semidefinite and polyhedral relaxations

Franz Rendl, Giovanni Rinaldi, Angelika Wiegele

Mathematical Programming•Jul 21, 2008•6 citations

Approximation bounds for quadratic maximization and max-cut problems with semidefinite programming relaxation

Dachuan Xu, Shu-zhong Zhang

Science in China Series A Mathematics•Jul 30, 2007•7 citations

Improved semidefinite bounding procedure for solving Max-Cut problems to optimality

Nathan Krislock, Jérôme Malick, Frédéric Roupin

Mathematical Programming•Oct 13, 2012•55 citations

Semidefinite programming approaches for stable set and max-cut problems

Lennart Sinjorgo

This thesis investigates the use of semidefinite programming (SDP) for solving (variants of) the well-known stable set and max-cut problems. Chapter 2 considers a generalization of the stable set problem, and a corresponding SDP relaxation. Similarly, Chapter 3 considers a generalization of the max-cut problem that involves complex roots of unity. Various complex SDP relaxations of this generalized max-cut problem are studied. Chapter 4 provides approximation algorithms for the quantum generalization of the max-cut problem. These approximation algorithms employ a hierarchy of SDP relaxations for noncommutative polynomial optimization problems. Chapter 5 provides an SDP algorithm for computing bounds on the stability numbers of graphs. Chapter 6 provides an SDP algorithm for solving the MAX-SAT problem. Chapters 5 and 6 provide extensive numerical results on these algorithms.

•Jan 20, 2026•0 citations

BiqBin: A Parallel Branch-and-bound Solver for Binary Quadratic Problems with Linear Constraints

Nicolò Gusmeroli, Timotej Hrga, Borut Lužar +3 more

We present BiqBin, an exact solver for linearly constrained binary quadratic problems. Our approach is based on an exact penalty method to first efficiently transform the original problem into an instance of Max-Cut, and then to solve the Max-Cut problem by a branch-and-bound algorithm. All the main ingredients are carefully developed using new semidefinite programming relaxations obtained by strengthening the existing relaxations with a set of hypermetric inequalities, applying the bundle method as the bounding routine and using new strategies for exploring the branch-and-bound tree. Furthermore, an efficient C implementation of a sequential and a parallel branch-and-bound algorithm is presented. The latter is based on a load coordinator-worker scheme using MPI for multi-node parallelization and is evaluated on a high-performance computer. The new solver is benchmarked against BiqCrunch, GUROBI, and SCIP on four families of (linearly constrained) binary quadratic problems. Numerical results demonstrate that BiqBin is a highly competitive solver. The serial version outperforms the other three solvers on the majority of the benchmark instances. We also evaluate the parallel solver and show that it has good scaling properties. The general audience can use it as an on-line service available at http://www.biqbin.eu .

arXiv:2009.06240•ACM Transactions on Mathematical Software•Jun 30, 2022•19 citations

Feasible direction algorithm for solving the SDP relaxations of quadratic {−1, 1} programming problems

Liu Hongwei*, Xinhui Wang, Liu Sanyang

In this article, we propose a feasible direction algorithm for solving the semidefinite programming (SDP) relaxations of quadratic {−1, 1} programming problems. This algorithm's distinguishing features are that it uses a low rank factorization and searches with a constant step-size. Its convergence is also proven. Finally, we report some numerical examples to compare our method with the low rank factorization method of Burer and Monteiro on the SDP relaxation of the max-cut problem. *E-mail: xdliuhongwei@hotmail.com

Optimization methods & software•Apr 1, 2004•13 citations
This thesis investigates the use of semidefinite programming (SDP) for solving (variants of) the well-known stable set and max-cut problems. Chapter 2 considers a generalization of the stable set problem, and a corresponding SDP relaxation. Similarly, Chapter 3 considers a generalization of the max-cut problem that involves complex roots of unity. Various complex SDP relaxations of this generalized max-cut problem are studied. Chapter 4 provides approximation algorithms for the quantum generalization of the max-cut problem. These approximation algorithms employ a hierarchy of SDP relaxations for noncommutative polynomial optimization problems. Chapter 5 provides an SDP algorithm for computing bounds on the stability numbers of graphs. Chapter 6 provides an SDP algorithm for solving the MAX-SAT problem. Chapters 5 and 6 provide extensive numerical results on these algorithms.
We present BiqBin, an exact solver for linearly constrained binary quadratic problems. Our approach is based on an exact penalty method to first efficiently transform the original problem into an instance of Max-Cut, and then to solve the Max-Cut problem by a branch-and-bound algorithm. All the main ingredients are carefully developed using new semidefinite programming relaxations obtained by strengthening the existing relaxations with a set of hypermetric inequalities, applying the bundle method as the bounding routine and using new strategies for exploring the branch-and-bound tree. Furthermore, an efficient C implementation of a sequential and a parallel branch-and-bound algorithm is presented. The latter is based on a load coordinator-worker scheme using MPI for multi-node parallelization and is evaluated on a high-performance computer. The new solver is benchmarked against BiqCrunch, GUROBI, and SCIP on four families of (linearly constrained) binary quadratic problems. Numerical results demonstrate that BiqBin is a highly competitive solver. The serial version outperforms the other three solvers on the majority of the benchmark instances. We also evaluate the parallel solver and show that it has good scaling properties. The general audience can use it as an on-line service available at http://www.biqbin.eu .
In this article, we propose a feasible direction algorithm for solving the semidefinite programming (SDP) relaxations of quadratic {−1, 1} programming problems. This algorithm's distinguishing features are that it uses a low rank factorization and searches with a constant step-size. Its convergence is also proven. Finally, we report some numerical examples to compare our method with the low rank factorization method of Burer and Monteiro on the SDP relaxation of the max-cut problem. *E-mail: xdliuhongwei@hotmail.com