A fast branch-and-bound algorithm for non-convex quadratic integer optimization subject to linear constraints using ellipsoidal relaxations.
Christoph BuchheimMarianna De SantisLaura PalagiPublished in: Oper. Res. Lett. (2015)
Keyphrases
- branch and bound algorithm
- lower bound
- convex quadratic
- subject to linear constraints
- upper bounding
- np hard
- combinatorial optimization
- integer variables
- upper bound
- branch and bound
- inequality constraints
- optimal solution
- nonlinear programming
- optimization problems
- linear programming
- constrained optimization
- linear programming relaxation
- feature selection
- decision variables
- kernel learning
- quadratic programming
- search algorithm
- simulated annealing