On Finitely Terminating Branch-and-Bound Algorithms for Some Global Optimization Problems
SIAM Journal on Optimization
Semidefinite Relaxations of Fractional Programs via Novel Convexification Techniques
Journal of Global Optimization
Global optimization of mixed-integer nonlinear programs: A theoretical and computational study
Mathematical Programming: Series A and B
Cuts for mixed 0-1 conic programming
Mathematical Programming: Series A and B
Interval Analysis on Directed Acyclic Graphs for Global Optimization
Journal of Global Optimization
Perspective cuts for a class of convex 0–1 mixed integer programs
Mathematical Programming: Series A and B
An Exact Reformulation Algorithm for Large Nonconvex NLPs Involving Bilinear Terms
Journal of Global Optimization
Lifting inequalities: a framework for generating strong cuts for nonlinear programs
Mathematical Programming: Series A and B
Branching and bounds tighteningtechniques for non-convex MINLP
Optimization Methods & Software - GLOBAL OPTIMIZATION
Conic mixed-integer rounding cuts
Mathematical Programming: Series A and B
Global optimization problems and domain reduction strategies
Mathematical Programming: Series A and B
A computational comparison of reformulations of the perspective relaxation: SOCP vs. cutting planes
Operations Research Letters
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Interval-gradient cuts are (nonlinear) valid inequalities derived from continuously differentiable nonconvex constraints. In this paper we define interval-subgradient cuts, a generalization to nondifferentiable constraints, and show that no-good cuts with 1-norm are a special case of interval-subgradient cuts. We then briefly discuss what happens if other norms are used.