Search results
Results From The WOW.Com Content Network
A closed feasible region of a linear programming problem with three variables is a convex polyhedron. In mathematical optimization and computer science , a feasible region, feasible set, or solution space is the set of all possible points (sets of values of the choice variables) of an optimization problem that satisfy the problem's constraints ...
In some cases, infeasible problems are handled by minimizing a sum of feasibility violations. Some special cases of nonlinear programming have specialized solution methods: If the objective function is concave (maximization problem), or convex (minimization problem) and the constraint set is convex , then the program is called convex and ...
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.
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.
In operations research, the Big M method is a method of solving linear programming problems using the simplex algorithm.The Big M method extends the simplex algorithm to problems that contain "greater-than" constraints.
In mathematical optimization, the revised simplex method is a variant of George Dantzig's simplex method for linear programming.. The revised simplex method is mathematically equivalent to the standard simplex method but differs in implementation.
The storm is slated to depart the region "relatively quickly" on Sunday though Longley said some light snow may linger in New England, especially around Boston, coastal New Hampshire and Portland ...
Operations research also uses stochastic modeling and simulation to support improved decision-making. Increasingly, operations research uses stochastic programming to model dynamic decisions that adapt to events; such problems can be solved with large-scale optimization and stochastic optimization methods.