On modeling robust policies for financial trading
Optimization in industry 2
Computational study of a family of mixed-integer quadratic programming problems
Mathematical Programming: Series A and B
Perspective cuts for a class of convex 0–1 mixed integer programs
Mathematical Programming: Series A and B
Projected Perspective Reformulations with Applications in Design Problems
Operations Research
Transforming mathematical models using declarative reformulation rules
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Eigenvalue techniques for convex objective, nonconvex optimization problems
IPCO'10 Proceedings of the 14th international conference on Integer Programming and Combinatorial Optimization
A computational comparison of reformulations of the perspective relaxation: SOCP vs. cutting planes
Operations Research Letters
On valid inequalities for quadratic programming with continuous variables and binary indicators
IPCO'13 Proceedings of the 16th international conference on Integer Programming and Combinatorial Optimization
Journal of Global Optimization
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We present a new approach, requiring the solution of a SemiDefinite Program, for decomposing the Hessian of a nonseparable mixed-integer quadratic problem to permit using perspective cuts to improve its continuous relaxation bound. The new method favorably compares with a previously proposed one requiring a minimum eigenvalue computation.