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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.
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.
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).
A. The feasible set {b+L} ∩ K is bounded, and intersects the interior of the cone K. B. We are given in advance a strictly-feasible solution x^, that is, a feasible solution in the interior of K. C. We know in advance the optimal objective value, c*, of the problem. D. We are given an M-logarithmically-homogeneous self-concordant barrier F ...
The feasible region or solution space of the MAP is very large. The number K {\displaystyle K} of feasible solutions (the size of the MAP instance) depends on the MAP parameters D , N {\displaystyle D,N} .
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.
Feasibility may refer to: Feasibility study, a preliminary study to determine a project's viability "Feasibility Study" (The Outer Limits), an episode of The Outer Limits TV show; Feasible region, a region that satisfies mathematical constraints; Logical possibility, an achievable thing
Each problem p in the family is represented by a data-vector Data(p), e.g., the real-valued coefficients in matrices and vectors representing the function f and the feasible region G. The size of a problem p, Size(p), is defined as the number of elements (real numbers) in Data(p). The following assumptions are needed: G (the feasible region) is: