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This paper considers global optimization with a black-box unknown objectivefunction that can be non-convex and non-differentiable. Such a difficultoptimization problem arises in many real-world applications, such as parametertuning in machine learning, engineering design problem, and planning with acomplex physics simulator. This paper proposes a new global optimizationalgorithm, called Locally Oriented Global Optimization (LOGO), to aim for bothfast convergence in practice and finite-time error bound in theory. Theadvantage and usage of the new algorithm are illustrated via theoreticalanalysis and an experiment conducted with 11 benchmark test functions. Further,we modify the LOGO algorithm to specifically solve a planning problem viapolicy search with continuous state/action space and long time horizon whilemaintaining its finite-time error bound. We apply the proposed planning methodto accident management of a nuclear power plant. The result of the applicationstudy demonstrates the practical utility of our method.

Global Continuous Optimization with Error Bound and Fast Convergence