Search results
Results From The WOW.Com Content Network
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.
Informally, Slater's condition states that the feasible region must have an interior point (see technical details below). Slater's condition is a specific example of a constraint qualification. [2] In particular, if Slater's condition holds for the primal problem, then the duality gap is 0, and if the dual value is finite then it is attained.
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. If there exists an optimal solution, then there exists an optimal BFS.
The feasible set of the optimization problem consists of all points satisfying the inequality and the equality constraints. This set is convex because D {\displaystyle {\mathcal {D}}} is convex, the sublevel sets of convex functions are convex, affine sets are convex, and the intersection of convex sets is convex.
Feasible region, a region that satisfies mathematical constraints; Logical possibility, an achievable thing; See also. Pragmatism This page was last edited on 17 ...
Edexcel (also known since 2013 as Pearson Edexcel) [2] is a British multinational education and examination body formed in 1996 and wholly owned by Pearson plc since 2005. It is the only privately owned examination board in the United Kingdom. [3] Its name is a portmanteau term combining the words education and excellence.
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 ...
For each combinatorial optimization problem, there is a corresponding decision problem that asks whether there is a feasible solution for some particular measure m 0. For example, if there is a graph G which contains vertices u and v , an optimization problem might be "find a path from u to v that uses the fewest edges".