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  2. Karush–Kuhn–Tucker conditions - Wikipedia

    en.wikipedia.org/wiki/Karush–Kuhn–Tucker...

    Consider the following nonlinear optimization problem in standard form: . minimize () subject to (),() =where is the optimization variable chosen from a convex subset of , is the objective or utility function, (=, …,) are the inequality constraint functions and (=, …,) are the equality constraint functions.

  3. Lagrange multiplier - Wikipedia

    en.wikipedia.org/wiki/Lagrange_multiplier

    The Lagrange multiplier theorem states that at any local maximum (or minimum) of the function evaluated under the equality constraints, if constraint qualification applies (explained below), then the gradient of the function (at that point) can be expressed as a linear combination of the gradients of the constraints (at that point), with the ...

  4. Quadratic programming - Wikipedia

    en.wikipedia.org/wiki/Quadratic_programming

    An open source computational geometry package which includes a quadratic programming solver. CPLEX: Popular solver with an API (C, C++, Java, .Net, Python, Matlab and R). Free for academics. Excel Solver Function: A nonlinear solver adjusted to spreadsheets in which function evaluations are based on the recalculating cells.

  5. Constrained optimization - Wikipedia

    en.wikipedia.org/wiki/Constrained_optimization

    Constraints can be either hard constraints, which set conditions for the variables that are required to be satisfied, or soft constraints, which have some variable values that are penalized in the objective function if, and based on the extent that, the conditions on the variables are not satisfied.

  6. Functional programming - Wikipedia

    en.wikipedia.org/wiki/Functional_programming

    Lisp functions were defined using Church's lambda notation, extended with a label construct to allow recursive functions. [41] Lisp first introduced many paradigmatic features of functional programming, though early Lisps were multi-paradigm languages , and incorporated support for numerous programming styles as new paradigms evolved.

  7. Convex optimization - Wikipedia

    en.wikipedia.org/wiki/Convex_optimization

    In the standard form it is possible to assume, without loss of generality, that the objective function f is a linear function.This is because any program with a general objective can be transformed into a program with a linear objective by adding a single variable t and a single constraint, as follows: [9]: 1.4

  8. Constraint satisfaction problem - Wikipedia

    en.wikipedia.org/wiki/Constraint_satisfaction...

    Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite constraints over variables , which is solved by constraint satisfaction methods.

  9. Class invariant - Wikipedia

    en.wikipedia.org/wiki/Class_invariant

    An object invariant, or representation invariant, is a computer programming construct consisting of a set of invariant properties that remain uncompromised regardless of the state of the object. This ensures that the object will always meet predefined conditions, and that methods may, therefore, always reference the object without the risk of ...