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  2. File:Poloni's two objective function.pdf - Wikipedia

    en.wikipedia.org/wiki/File:Poloni's_two_objective...

    Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.

  3. Mathematical optimization - Wikipedia

    en.wikipedia.org/wiki/Mathematical_optimization

    The function f is variously called an objective function, criterion function, loss function, cost function (minimization), [8] utility function or fitness function (maximization), or, in certain fields, an energy function or energy functional. A feasible solution that minimizes (or maximizes) the objective function is called an optimal solution.

  4. File:Schaffer function 2 - multi-objective.pdf - Wikipedia

    en.wikipedia.org/wiki/File:Schaffer_function_2...

    Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.

  5. Constrained optimization - Wikipedia

    en.wikipedia.org/wiki/Constrained_optimization

    If all the hard constraints are linear and some are inequalities, but the objective function is quadratic, the problem is a quadratic programming problem. It is one type of nonlinear programming. It can still be solved in polynomial time by the ellipsoid method if the objective function is convex; otherwise the problem may be NP hard.

  6. Multi-objective optimization - Wikipedia

    en.wikipedia.org/wiki/Multi-objective_optimization

    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.

  7. Linear-fractional programming - Wikipedia

    en.wikipedia.org/wiki/Linear-fractional_programming

    Whereas the objective function in a linear program is a linear function, the objective function in a linear-fractional program is a ratio of two linear functions. A linear program can be regarded as a special case of a linear-fractional program in which the denominator is the constant function 1.

  8. Convex optimization - Wikipedia

    en.wikipedia.org/wiki/Convex_optimization

    In LP, the objective and constraint functions are all linear. Quadratic programming are the next-simplest. In QP, the constraints are all linear, but the objective may be a convex quadratic function. Second order cone programming are more general. Semidefinite programming are more general. Conic optimization are even more general - see figure ...

  9. Nonlinear programming - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_programming

    If the objective function is concave (maximization problem), or convex (minimization problem) and the constraint set is convex, then the program is called convex and general methods from convex optimization can be used in most cases. If the objective function is quadratic and the constraints are linear, quadratic programming techniques are used.