Pierre, Donald A. Department of Electrical Engineering, College of Engineering, Montana State University, Bozeman, Montana.
- Framework for Optimization
- System models
- Feasible solutions
- Performance measures
- Unconstrained Optimization
- Points of zero slope
- Second-order conditions
- Global versus local
- Numerical solution methods
- Gradient-based methods
- Constrained Optimization with Single Performance Measure
- Nonlinear programming
- Linear programming
- Multiobjective Optimization
- Efficient solutions
- Approaches to solution
- Dynamic Optimization
- Calculus of variations
- Dynamic programming
- Other Optimization Topics
- Stochastic optimization
- Multiple decision makers
- Combinatorial optimization
- Computer codes for solving optimization problems
- Suboptimal solutions
- Related Primary Literature
- Additional Reading
The design and operation of systems or processes to make them as good as possible in some defined sense. The approaches to optimizing systems are varied and depend on the type of system involved, but the goal of all optimization procedures is to obtain the best results possible (again, in some defined sense) subject to restrictions or constraints that are imposed. While a system may be optimized by treating the system itself, by adjusting various parameters of the process in an effort to obtain better results, it generally is more economical to develop a model of the process and to analyze performance changes that result from adjustments in the model. In many applications, the process to be optimized can be formulated as a mathematical model; with the advent of high-speed computers, very large and complex systems can be modeled, and optimization can yield substantially improved benefits. See also: Digital computer
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