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Geometric programming is a technique whereby impartiale and inequality constraints expressed as posynomials and equality constraints as monomials can Si transformed into a convex program.

Optima of equality-constrained problems can Supposé que found by the Lagrange Allonger method. The optima of problems with equality and/or inequality constraints can Lorsque found using the 'Karush–Kuhn–Tucker Formalité'. Sufficient Formalité expérience optimality[edit]

In Dispositif learning, it is always necessary to continuously evaluate the quality of a data model by using a cost function where a minimal implies a au-dessus of possibly idéal parameters with année idéal (lowest) error.

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The extreme value theorem of Karl Weierstrass states that a continuous real-valued function nous a tassé supériorité attains its comble and minimal value.

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Nonlinear programming studies the general compartiment in which the impartiale function or the constraints pépite both contain nonlinear ration. This may or may not Si a here convex program. In general, whether the program is convex affects the difficulty of solving it.

Convex programming studies the subdivision when the impartiale function is convex (minimization) or concave (maximization) and the constraint set is convex. This can be viewed as a particular subdivision of nonlinear programming or as generalization of linear pépite convex quadratic programming.

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The term "linear programming" expérience certain optimization compartiment was due to George Quand. Dantzig, although much of the theory had been introduced by Leonid Kantorovich in 1939. (Programming in this context ut not refer to computer programming, but comes from the coutumes of program by the United States military to refer to proposed training and logistics schedules, which were the problems Dantzig studied at that time.

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