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For example, to optimize a structural design, one would desire a design that is both light and rigid. When two objectives conflict, a trade-off must be created. There may be one lightest design, one stiffest design, and an infinite number of designs that are some compromise of weight and rigidity.
Statistical Science. 14 (2): 174– 196. doi: 10.1214/ss/1009212244. JSTOR 2676737. MR 1722074. Pukelsheim, Friedrich (2006). Optimal design of experiments. Classics in Applied Mathematics. Vol. 50 (republication with errata-list and new preface of Wiley (0-471-61971-X) 1993 ed.). Society for Industrial and Applied Mathematics. pp. 454+xxxii.
Optimization control (dynamic) – This is used largely in computer science and electrical engineering. The optimal control is per state and the results change in each of them. One can use mathematical programming, as well as dynamic programming. In this scenario, simulation can generate random samples and solve complex and large-scale problems ...
Design optimization is an engineering design methodology using a mathematical formulation of a design problem to support selection of the optimal design among many alternatives.
In mathematics, engineering, computer science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions.. Optimization problems can be divided into two categories, depending on whether the variables are continuous or discrete:
A science project is an educational activity for students involving experiments or construction of models in one of the science disciplines. Students may present their science project at a science fair, so they may also call it a science fair project. Science projects may be classified into four main types.
In computer science, program optimization, code optimization, or software optimization is the process of modifying a software system to make some aspect of it work more efficiently or use fewer resources. [1]
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.