  Based on our \bl{\textbf{Random Duality Theory (RDT)}}, in a sequence of our
recent papers \cite{Stojnicclupint19,Stojnicclupcmpl19,Stojnicclupplt19}, we
introduced a powerful algorithmic mechanism (called \bl{\textbf{CLuP}}) that
can be utilized to solve \textbf{\emph{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
\cite{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 \textbf{\emph{constrained}}
optimization into an \textbf{\emph{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 \cite{Stojnicclupint19,Stojnicclupcmpl19,Stojnicclupplt19} can solve.
