A simplex algorithm for piecewise-linear programming 11: finiteness, feasibility and degeneracy
Mathematical Programming: Series A and B
A simplex algorithm for piecewise-linear programming III: computational analysis and application
Mathematical Programming: Series A and B
Discontinuous piecewise linear optimization
Mathematical Programming: Series A and B
Journal of Optimization Theory and Applications
Cutting Planes for Low-Rank-Like Concave Minimization Problems
Operations Research
Some new Farkas-type results for inequality systems with DC functions
Journal of Global Optimization
Efficient heuristics for inventory placement in acyclic networks
Computers and Operations Research
On the hinge-finding algorithm for hingeing hyperplanes
IEEE Transactions on Information Theory
Generalization of hinging hyperplanes
IEEE Transactions on Information Theory
Hinging hyperplanes for regression, classification, and function approximation
IEEE Transactions on Information Theory
Operations Research Letters
Models for representing piecewise linear cost functions
Operations Research Letters
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This paper studies the problem of minimizing hinging hyperplanes (HH) which is a widely applied nonlinear model. To deal with HH minimization, we transform it into a d.c. (difference of convex functions) programming and a concave minimization on a polyhedron, then some mature techniques are applicable. More importantly, HH is a continuous piecewise linear function and for concave HH, the super-level sets are polyhedra. Inspired by this property, we establish a method which searches on the counter map in order to escape a local optimum. Intuitively, this method bypasses the super-level set and is hence called hill detouring method, following the name of hill climbing. In numerical experiments, the proposed algorithm is compared with CPLEX and a heuristic algorithm showing its effectiveness.