Algorithmic random duality theory -- large scale CLuP
Abstract
Based on our Random Duality Theory (RDT), in a sequence of our recent papers Stojnicclupint19,Stojnicclupcmpl19,Stojnicclupplt19, we introduced a powerful algorithmic mechanism (called CLuP) that can be utilized to solve exactly NP hard optimization problems in polynomial time. Here we move things further and utilize another of remarkable RDT features that we established in a long line of work in StojnicCSetam09,StojnicCSetamBlock09,StojnicISIT2010binary,StojnicDiscPercp13,StojnicUpper10,StojnicGenLasso10,StojnicGenSocp10,StojnicPrDepSocp10,StojnicRegRndDlt10,Stojnicbinary16fin,Stojnicbinary16asym. Namely, besides being stunningly precise in characterizing the performance of various random structures and optimization problems, RDT simultaneously also provided an almost unparallel way for creating computationally efficient optimization algorithms that achieve such performance. One of the keys to our success was our ability to transform the initial constrained optimization into an unconstrained one and in doing so greatly simplify things both conceptually and computationally. That ultimately enabled us to solve a large set of classical optimization problems on a very large scale level. Here, we demonstrate how such a thinking can be applied to CLuP as well and eventually utilized to solve pretty much any problem that the basic CLuP from Stojnicclupint19,Stojnicclupcmpl19,Stojnicclupplt19 can solve.