The spherical constraint in Boolean quadratic programs
From MaRDI portal
Publication:925238
DOI10.1007/s10898-007-9161-1zbMath1146.90043OpenAlexW2014548643MaRDI QIDQ925238
Publication date: 3 June 2008
Published in: Journal of Global Optimization (Search for Journal in Brave)
Full work available at URL: https://hal.inria.fr/inria-00070518/file/07-malick.pdf
Quadratic programming (90C20) Optimality conditions and duality in mathematical programming (90C46) Boolean programming (90C09)
Related Items
On the spherical quasi-convexity of quadratic functions, A variational approach of the rank function, On the bridge between combinatorial optimization and nonlinear optimization: a family of semidefinite bounds for 0--1 quadratic problems leading to quasi-Newton methods, Solving \(k\)-cluster problems to optimality with semidefinite programming, Semidefinite Approaches for MIQCP: Convex Relaxations and Practical Methods, A low complexity semidefinite relaxation for large-scale MIMO detection, Improved semidefinite bounding procedure for solving max-cut problems to optimality, Efficient semidefinite branch-and-cut for MAP-MRF inference, Improved row-by-row method for binary quadratic optimization problems, Constrained Best Euclidean Distance Embedding on a Sphere: A Matrix Optimization Approach
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Computing the nearest correlation matrix--a problem from finance
- Semi-Lagrangian relaxation applied to the uncapacitated facility location problem
- Solving the \(p\)-median problem with a semi-Lagrangian relaxation
- A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization
- Convex analysis and nonlinear optimization. Theory and examples
- Strengthened semidefinite relaxations via a second lifting for the Max-Cut problem
- Seizure warning algorithm based on optimization and nonlinear dynamics
- A recipe for semidefinite relaxation for \((0,1)\)-quadratic programming
- Lower bound improvement and forcing rule for quadratic binary programming
- Lectures on Modern Convex Optimization
- A Quadratically Convergent Newton Method for Computing the Nearest Correlation Matrix
- A nonlinear equation for linear programming
- An Implicit Enumeration Algorithm for Quadratic Integer Programming
- Iterative Solution of Linear Programs
- On the Shannon capacity of a graph
- Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
- A Spectral Bundle Method for Semidefinite Programming
- A Dual Approach to Semidefinite Least-Squares Problems
- Semidefinite Programming vs. LP Relaxations for Polynomial Programming
- A Comparison of the Sherali-Adams, Lovász-Schrijver, and Lasserre Relaxations for 0–1 Programming
- Introduction to global optimization.