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## User: priyam-das

### Title: Black-box Optimization on Multiple Simplex Constrained Blocks

Black-box optimization of objective function of parameters belonging to
simplex arises in many inference and predictive models. Das (2016) introduced
Greedy Co-ordinate Descent of Varying Step-sizes on Simplex (GCDVSS) which
efficiently optimizes any black-box function whose parameters belong to a
simplex. In this paper, that method has been modified and extended for the case
where the set of parameters may belong to multiple simplex block of different
sizes. The main principle of this algorithm is to make jumps of varying
step-sizes within each simplexes simultaneously and searching for the best
direction for movement. Since this algorithm is designed specially for multiple
simplex blocks parameter space, the proportion of movements made within the
parameter space during the update step of a iteration is relatively higher for
the proposed algorithm. Starting from a single initial guess, unlike genetic
algorithm or simulated annealing, requirement of parallelization for this
algorithm grows linearly with the dimension of the parameter space which makes
it more efficient for higher dimensional optimization problems. Comparative
studies with some existing algorithms have been provided based on modified
well-known benchmark functions. Upto 7 folds of improvement in computation time
has been noted for using the proposed algorithm over Genetic algorithm,
yielding significantly better solution in all the cases considered.

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