Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Resource allocation problems: algorithmic approaches
Resource allocation problems: algorithmic approaches
Introduction to algorithms
Efficient Resource Allocation with Non-Concave Objective Functions
Computational Optimization and Applications
Managing Large Scale Computational Markets
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 7 - Volume 7
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
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We consider resource allocation with separable objective functions defined over subranges of the integers. While it is well known that (the maximisation version of) this problem can be solved efficiently if the objective functions are concave, the general problem of resource allocation with functions that are not necessarily concave is difficult. In this paper we show that for a large class of problem instances with noisy objective functions the optimal solutions can be computed efficiently. We support our claims by experimental evidence. Our experiments show that our algorithm in hard and practically relevant cases runs up to 40 - 60 times faster than the standard method.