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  2. Constrained optimization - Wikipedia

    en.wikipedia.org/wiki/Constrained_optimization

    The constrained-optimization problem (COP) is a significant generalization of the classic constraint-satisfaction problem (CSP) model. [1] COP is a CSP that includes an objective function to be optimized.

  3. Design optimization - Wikipedia

    en.wikipedia.org/wiki/Design_optimization

    Design optimization involves the following stages: [1] [2] Variables: Describe the design alternatives; Objective: Elected functional combination of variables (to be maximized or minimized) Constraints: Combination of Variables expressed as equalities or inequalities that must be satisfied for any acceptable design alternative

  4. List of optimization software - Wikipedia

    en.wikipedia.org/wiki/List_of_optimization_software

    The use of optimization software requires that the function f is defined in a suitable programming language and connected at compilation or run time to the optimization software. The optimization software will deliver input values in A , the software module realizing f will deliver the computed value f ( x ) and, in some cases, additional ...

  5. Optimization problem - Wikipedia

    en.wikipedia.org/wiki/Optimization_problem

    g i (x) ≤ 0 are called inequality constraints; h j (x) = 0 are called equality constraints, and; m ≥ 0 and p ≥ 0. If m = p = 0, the problem is an unconstrained optimization problem. By convention, the standard form defines a minimization problem.

  6. Subgradient method - Wikipedia

    en.wikipedia.org/wiki/Subgradient_method

    3.2 General constraints. 4 See also. 5 References. ... [4] [5] However, they are ... The subgradient method can be extended to solve the inequality constrained problem

  7. Ellipsoid method - Wikipedia

    en.wikipedia.org/wiki/Ellipsoid_method

    Step 4: In the optimization problem min z f(z), we can assume that z is in a box of side-length 2 L, where L is the bit length of the problem data. Thus, we have a bounded convex program, that can be solved up to any accuracy ε by the ellipsoid method, in time polynomial in L .

  8. 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.

  9. Active-set method - Wikipedia

    en.wikipedia.org/wiki/Active-set_method

    In mathematical optimization, the active-set method is an algorithm used to identify the active constraints in a set of inequality constraints. The active constraints are then expressed as equality constraints, thereby transforming an inequality-constrained problem into a simpler equality-constrained subproblem.