Recognition problems for special classes of polynomials in 0-1 variables
Mathematical Programming: Series A and B
On the equivalence of paved-duality and standard linearization of nonlinear 0–1 optimization
Selected papers on First international colloquium on pseudo-boolean optimization and related topics
Annals of Operations Research
Concave extensions for nonlinear 0–1 maximization problems
Mathematical Programming: Series A and B
Analysis of Bounds for Multilinear Functions
Journal of Global Optimization
Semidefinite Relaxations of Fractional Programs via Novel Convexification Techniques
Journal of Global Optimization
Computational Optimization and Applications
Trilinear Monomials with Mixed Sign Domains: Facets of the Convex and Concave Envelopes
Journal of Global Optimization
Multiterm polyhedral relaxations for nonconvex, quadratically constrained quadratic programs
Optimization Methods & Software - GLOBAL OPTIMIZATION
Outer approximation algorithms for canonical DC problems
Journal of Global Optimization
On convex relaxations of quadrilinear terms
Journal of Global Optimization
Efficient pruning technique based on linear relaxations
COCOS'03 Proceedings of the Second international conference on Global Optimization and Constraint Satisfaction
Convex envelopes of products of convex and component-wise concave functions
Journal of Global Optimization
Compact relaxations for polynomial programming problems
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
Relaxations of multilinear convex envelopes: dual is better than primal
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
A new necessary and sufficient global optimality condition for canonical DC problems
Journal of Global Optimization
Distortion-aware scalable video streaming to multinetwork clients
IEEE/ACM Transactions on Networking (TON)
GloMIQO: Global mixed-integer quadratic optimizer
Journal of Global Optimization
Journal of Global Optimization
Hi-index | 0.00 |
Convex envelopes of multilinear functions on a unit hypercube arepolyhedral. This well-known fact makes the convex envelopeapproximation very useful in the linearization of non-linear 0–1programming problems and in global bilinear optimization. This paperpresents necessary and sufficient conditions for a convex envelope to be apolyhedral function and illustrates how these conditions may be used inconstructing of convex envelopes. The main result of the paper is a simpleanalytical formula, which defines some faces of the convex envelope of amultilinear function. This formula proves to be a generalization of the wellknown convex envelope formula for multilinear monomial functions.