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Building upon our earlier work of a martingale approach to globaloptimization, a powerful stochastic search scheme for the global optimum ofcost functions is proposed on the basis of change of measures on the statesthat evolve as diffusion processes and splitting of the state-space along thelines of a Bayesian game. To begin with, the efficacy of the optimizer, whencontrasted with one of the most efficient existing schemes, is assessed againsta family of Np-hard benchmark problems. Then, using both simulated- andexperimental data, potentialities of the new proposal are further explored inthe context of an inverse problem of significance in medical imaging, whereinthe superior reconstruction features of a global search vis-\`a-vis thecommonly adopted local or quasi-local schemes are brought into relief.

A Stochastically Evolving Non-local Search and Solutions to Inverse Problems with Sparse Data