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2. The strong duality theorem provides a "good characterization" of the optimal value of an LP in that it allows us to easily prove that some value t is the optimum of some LP. The proof proceeds in two steps: [4]: 260–261 Show a feasible solution to the primal LP with value t; this proves that the optimum is at least t.
There are algorithms for solving an LP in weakly-polynomial time, such as the ellipsoid method; however, they usually return optimal solutions that are not basic. However, Given any optimal solution to the LP, it is easy to find an optimal feasible solution that is also basic. [2]: see also "external links" below.
The Lagrangian dual problem is obtained by forming the Lagrangian of a minimization problem by using nonnegative Lagrange multipliers to add the constraints to the objective function, and then solving for the primal variable values that minimize the original objective function. This solution gives the primal variables as functions of the ...
Some of the local methods assume that the graph admits a perfect matching; if this is not the case, then some of these methods might run forever. [1]: 3 A simple technical way to solve this problem is to extend the input graph to a complete bipartite graph, by adding artificial edges with very large weights. These weights should exceed the ...
If we denote the area of land planted with wheat and barley by x 1 and x 2 respectively, then profit can be maximized by choosing optimal values for x 1 and x 2. This problem can be expressed with the following linear programming problem in the standard form:
Figure 2: A paraboloid constrained along two intersecting lines. Figure 3: Contour map of Figure 2. The method of Lagrange multipliers can be extended to solve problems with multiple constraints using a similar argument. Consider a paraboloid subject to two line constraints that intersect at a single point. As the only feasible solution, this ...
A series of linear programming constraints on two variables produce a region of possible values for those variables. Solvable two-variable problems will have a feasible region in the shape of a convex simple polygon if it is bounded. In an algorithm that tests feasible points sequentially, each tested point is in turn a candidate solution.
The advantage of the penalty method is that, once we have a penalized objective with no constraints, we can use any unconstrained optimization method to solve it. The disadvantage is that, as the penalty coefficient p grows, the unconstrained problem becomes ill-conditioned - the coefficients are very large, and this may cause numeric errors ...