UCLA

This work presents a novel method to identify the global optimum of a general class of single parameter optimization problems, typically arising in optimizing sequential processes. Capitalizing on the problem’s necessary conditions of optimality, the algorithm identifies arbitrarily tight upper and lower bounding envelopes for the graph of the global optimum as a function of the optimization problem’s single parameter. A challenging case study is presented illustrating the algorithm’s global optimum identification capabilities.