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Applications of Constrained Optimization

Abstract

Constrained optimization provides a general framework in which a variety of design criteria and specifications can be readily imposed on the required solution. Usually, a multivariable objective function that quantifies a performance measure of a design can be identified. This objective function may be linear, quadratic, or highly nonlinear, and usually it is differentiable so that its gradient and sometimes Hessian can be evaluated. In a real-life design problem, the design is carried out under certain physical limitations with limited resources. If these limitations can be quantified as equality or inequality constraints on the design variables, then a constrained optimization problem can be formulated whose solution leads to an optimal design that satisfies the limitations imposed. Depending on the degree of nonlinearity of the objective function and constraints, the problem at hand can be a linear programming (LP), quadratic programming (QP), convex programming (CP), semidefinite programming (SDP), second-order cone programming (SOCP), or general nonlinear constrained optimization problem.

Keywords

Contact Force Model Predictive Control Multiuser Detector Robot Hand Dextrous Hand 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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