  It is a well known fact that sequential algorithms which exhibit a strong
"local" nature can be adapted to the distributed setting given a legal graph
coloring. The running time of the distributed algorithm will then be at least
the number of colors. Surprisingly, this well known idea was never formally
stated as a unified framework. In this paper we aim to define a robust family
of local sequential algorithms which can be easily adapted to the distributed
setting. We then develop new tools to further enhance these algorithms,
achieving state of the art results for fundamental problems.
  We define a simple class of greedy-like algorithms which we call
\emph{orderless-local} algorithms. We show that given a legal $c$-coloring of
the graph, every algorithm in this family can be converted into a distributed
algorithm running in $O(c)$ communication rounds in the CONGEST model. We show
that this family is indeed robust as both the method of conditional
expectations and the unconstrained submodular maximization algorithm of
Buchbinder \etal \cite{BuchbinderFNS15} can be expressed as orderless-local
algorithms for \emph{local utility functions} --- Utility functions which have
a strong local nature to them.
  We use the above algorithms as a base for new distributed approximation
algorithms for the weighted variants of some fundamental problems: Max $k$-Cut,
Max-DiCut, Max 2-SAT and correlation clustering. We develop algorithms which
have the same approximation guarantees as their sequential counterparts, up to
a constant additive $\epsilon$ factor, while achieving an $O(\log^* n)$ running
time for deterministic algorithms and $O(\epsilon^{-1})$ running time for
randomized ones. This improves exponentially upon the currently best known
algorithms.
