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  2. Feasible region - Wikipedia

    en.wikipedia.org/wiki/Feasible_region

    The space of all candidate solutions, before any feasible points have been excluded, is called the feasible region, feasible set, search space, or solution space. [2] This is the set of all possible solutions that satisfy the problem's constraints. Constraint satisfaction is the process of finding a point in the feasible set.

  3. Interior-point method - Wikipedia

    en.wikipedia.org/wiki/Interior-point_method

    An interior point method was discovered by Soviet mathematician I. I. Dikin in 1967. [1] The method was reinvented in the U.S. in the mid-1980s. In 1984, Narendra Karmarkar developed a method for linear programming called Karmarkar's algorithm, [2] which runs in provably polynomial time (() operations on L-bit numbers, where n is the number of variables and constants), and is also very ...

  4. Basic feasible solution - Wikipedia

    en.wikipedia.org/wiki/Basic_feasible_solution

    In the theory of linear programming, a basic feasible solution (BFS) is a solution with a minimal set of non-zero variables. Geometrically, each BFS corresponds to a vertex of the polyhedron of feasible solutions.

  5. Linear programming - Wikipedia

    en.wikipedia.org/wiki/Linear_programming

    A closed feasible region of a problem with three variables is a convex polyhedron. The surfaces giving a fixed value of the objective function are planes (not shown). The linear programming problem is to find a point on the polyhedron that is on the plane with the highest possible value.

  6. Simplex algorithm - Wikipedia

    en.wikipedia.org/wiki/Simplex_algorithm

    The possible results of Phase I are either that a basic feasible solution is found or that the feasible region is empty. In the latter case the linear program is called infeasible. In the second step, Phase II, the simplex algorithm is applied using the basic feasible solution found in Phase I as a starting point.

  7. Nonlinear programming - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_programming

    The blue region is the feasible region. The tangency of the line with the feasible region represents the solution. The line is the best achievable contour line (locus with a given value of the objective function).

  8. Constraint satisfaction - Wikipedia

    en.wikipedia.org/wiki/Constraint_satisfaction

    A solution is therefore an assignment of values to the variables that satisfies all constraints—that is, a point in the feasible region. The techniques used in constraint satisfaction depend on the kind of constraints being considered.

  9. Feasibility - Wikipedia

    en.wikipedia.org/wiki/Feasibility

    Feasible region, a region that satisfies mathematical constraints; Logical possibility, an achievable thing; See also. Pragmatism This page was last edited on 17 ...