Coupled optimization in protein docking
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
A Radial Basis Function Method for Global Optimization
Journal of Global Optimization
Convex Quadratic Approximation
Computational Optimization and Applications
Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions
Journal of Global Optimization
Global Minimization via Piecewise-Linear Underestimation
Journal of Global Optimization
Iterative Convex Quadratic Approximation for Global Optimization in Protein Docking
Computational Optimization and Applications
Convex Kernel Underestimation of Functions with Multiple Local Minima
Computational Optimization and Applications
Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In several applications, underestimation of functions has proven to be a helpful tool for global optimization. In protein---ligand docking problems as well as in protein structure prediction, single convex quadratic underestimators have been used to approximate the location of the global minimum point. While this approach has been successful for basin-shaped functions, it is not suitable for energy functions with more than one distinct local minimum with a large magnitude. Such functions may contain several basin-shaped components and, thus, cannot be underfitted by a single convex underestimator. In this paper, we propose using an underestimator composed of several negative Gaussian functions. Such an underestimator can be computed by solving a nonlinear programming problem, which minimizes the error between the data points and the underestimator in the L 1 norm. Numerical results for simulated and actual docking energy functions are presented.