Texte intégral
Documents relatifs
Index Terms—Energy efficiency, exact line search, LASSO, massive MIMO, MIMO broadcast channel, nonconvex optimiza- tion, nondifferentiable optimization, pseudo-convex
A combinatorial algorithm minimizing submodular functions in strongly polynomial time. Journal of Combinatorial Theory, Series B,
We have so far studied the minimization of separable convex functions over submodular polytopes, motivated by bottlenecks in projection-based first-order optimization methods in
However, if on the one hand convex optimization usually refers to minimizing a convex func- tion on a convex set K without precising its representation (g j ) (see e.g. [3, Chapter
(1) In Section 3, we cast the problem of minimizing decomposable submodular functions as an or- thogonal projection problem and show how existing optimization techniques may be
In this paper, we show that for maximizing non-monotone DR-submodular functions over a general convex set (such as up-closed convex sets, conic convex set, etc) the Frank-Wolfe
Theorem ( Gr¨otschel-Lov´asz-Schrijver, Fujishige-Fleicher-Iwata, Schrijver ) The minimum value of a submodular function can be found
Supermodular functions, and their duals, submodular functions, play a central rˆ ole in many fields of discrete mathematics, most notably combinatorial optimization (rank function