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A Risk-Based Approach to Optimisation under Limited Information

A risk-based black box optimisation approach is presented that addresses a class of nonconvex multi-variable optimisation problems often encountered in wired and wireless network resource allocation. Information limitations both in terms of search budget and lack of knowledge on the objective function play a defining role in such problems. A novel algorithm is introduced that reduces a risk-like metric in a greedy manner. This risk metric strikes a balance between the conflicting information acquisition and function maximisation objectives. Hence, it brings a novel perspective to the problem. Initial numerical analysis indicates that the presented algorithm is more robust than the alternatives in [2] possibly due to its emphasis on risk reduction.