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  2. Linear programming - Wikipedia

    en.wikipedia.org/wiki/Linear_programming

    Standard form is the usual and most intuitive form of describing a linear programming problem. It consists of the following three parts: A linear (or affine) function to be maximized; e.g. (,) = + Problem constraints of the following form; e.g.

  3. 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.

  4. Convex optimization - Wikipedia

    en.wikipedia.org/wiki/Convex_optimization

    Many optimization problems can be equivalently formulated in this standard form. For example, the problem of maximizing a concave function can be re-formulated equivalently as the problem of minimizing the convex function . The problem of maximizing a concave function over a convex set is commonly called a convex optimization problem.

  5. Simplex algorithm - Wikipedia

    en.wikipedia.org/wiki/Simplex_algorithm

    The transformation of a linear program to one in standard form may be accomplished as follows. [16] First, for each variable with a lower bound other than 0, a new variable is introduced representing the difference between the variable and bound. The original variable can then be eliminated by substitution. For example, given the constraint

  6. Second-order cone programming - Wikipedia

    en.wikipedia.org/wiki/Second-order_cone_programming

    The coneprog function solves SOCP problems [12] using an interior-point algorithm [13] MOSEK: commercial: parallel interior-point algorithm NAG Numerical Library: commercial: General purpose numerical library with SOCP solver SCS: open source SCS (Splitting Conic Solver) is a numerical optimization package for solving large-scale convex ...

  7. Dual linear program - Wikipedia

    en.wikipedia.org/wiki/Dual_linear_program

    We use this example to illustrate the proof of the weak duality theorem. Suppose that, in the primal LP, we want to get an upper bound on the objective 3 x 1 + 4 x 2 {\displaystyle 3x_{1}+4x_{2}} . We can use the constraint multiplied by some coefficient, say y 1 {\displaystyle y_{1}} .

  8. Semidefinite programming - Wikipedia

    en.wikipedia.org/wiki/Semidefinite_programming

    A linear programming problem is one in which we wish to maximize or minimize a linear objective function of real variables over a polytope.In semidefinite programming, we instead use real-valued vectors and are allowed to take the dot product of vectors; nonnegativity constraints on real variables in LP (linear programming) are replaced by semidefiniteness constraints on matrix variables in ...

  9. Optimization problem - Wikipedia

    en.wikipedia.org/wiki/Optimization_problem

    The standard form of a continuous optimization problem is [1] ... the standard form defines a minimization problem. ... For example, if there is a graph G ...