In mathematics, optimization is the discipline which is concerned with finding the maxima and minima of functions, possibly subject to constraints. An example of an optimization problem is the following: maximize the profit of a manufacturing operation while ensuring that none of the resources exceed certain limits and also satisfying as much of the demand faced as possible. Optimization has many practical applications in logistics and design problems.
In computer science, optimization is the process of improving a system in certain ways to increase the effective execution speed and/or bandwidth, or reducing memory requirements. Despite its name, optimization does not necessarily mean finding the optimum solution to a problem. Often this is not possible, and heuristic algorithms must be used instead.
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He is especially interested in problems of legislative apportionment, in the connections between apportionment methods and issues in local/global optimization, and in axiomatic approaches and related impossibility theorems.
Shane Henderson does research on simulation optimization, that is, on optimization problems where the objective function and/or constraint functions are evaluated using simulation.
He also is intrigued by optimization problems that are naturally viewed from the perspective of functional analysis and its rich duality theory.
The goal of the theory is the creation of reliable methods to catch the extremum of a function by an intelligent arrangement of its evaluations (measurements).
Optimizationtheory is developed by ingenious and creative people, who regularly appeal to vivid common sense associations, formulating them in a general mathematical form.
Inaccuracy of the model is emphasized in optimization problem, since optimization usually brings the control parameters to the edge, where a model may fail to accurately describe the prototype.
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