  This paper reviews recent advances in big data optimization, providing the
state-of-art of this emerging field. The main focus in this review are
optimization techniques being applied in big data analysis environments.
Integer linear programming, coordinate descent methods, alternating direction
method of multipliers, simulation optimization and metaheuristics like
evolutionary and genetic algorithms, particle swarm optimization, differential
evolution, fireworks, bat, firefly and cuckoo search algorithms implementations
are reviewed and discussed. The relation between big data optimization and
software engineering topics like information work-flow styles, software
architectures, and software framework is discussed. Comparative analysis in
platforms being used in big data optimization environments are highlighted in
order to bring a state-or-art of possible architectures and topologies.
