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  2. LP-type problem - Wikipedia

    en.wikipedia.org/wiki/LP-type_problem

    In this formulation, the set S is the set of all vertices in both polytopes, and the function value f(A) is the negation of the smallest distance between the convex hulls of the two subsets A of vertices in the two polytopes. The combinatorial dimension of the problem is d + 1 if the two polytopes are disjoint, or d + 2 if they have a nonempty ...

  3. Dual linear program - Wikipedia

    en.wikipedia.org/wiki/Dual_linear_program

    The duality theorem states that the duality gap between the two LP problems is at least zero. Economically, it means that if the first factory is given an offer to buy its entire stock of raw material, at a per-item price of y, such that A T y ≥ c, y ≥ 0, then it should take the offer. It will make at least as much revenue as it could ...

  4. Linear programming - Wikipedia

    en.wikipedia.org/wiki/Linear_programming

    The simplex algorithm and its variants fall in the family of edge-following algorithms, so named because they solve linear programming problems by moving from vertex to vertex along edges of a polytope. This means that their theoretical performance is limited by the maximum number of edges between any two vertices on the LP polytope.

  5. Assignment problem - Wikipedia

    en.wikipedia.org/wiki/Assignment_problem

    One way to solve it is to invent a fourth dummy task, perhaps called "sitting still doing nothing", with a cost of 0 for the taxi assigned to it. This reduces the problem to a balanced assignment problem, which can then be solved in the usual way and still give the best solution to the problem.

  6. Basic feasible solution - Wikipedia

    en.wikipedia.org/wiki/Basic_feasible_solution

    For the definitions below, we first present the linear program in the so-called equational form: . maximize subject to = and . where: and are vectors of size n (the number of variables);

  7. Karmarkar's algorithm - Wikipedia

    en.wikipedia.org/wiki/Karmarkar's_algorithm

    Karmarkar's algorithm is an algorithm introduced by Narendra Karmarkar in 1984 for solving linear programming problems. It was the first reasonably efficient algorithm that solves these problems in polynomial time. The ellipsoid method is also polynomial time but proved to be inefficient in practice.

  8. Duality (optimization) - Wikipedia

    en.wikipedia.org/wiki/Duality_(optimization)

    In mathematical optimization theory, duality or the duality principle is the principle that optimization problems may be viewed from either of two perspectives, the primal problem or the dual problem. If the primal is a minimization problem then the dual is a maximization problem (and vice versa).

  9. Cutting-plane method - Wikipedia

    en.wikipedia.org/wiki/Cutting-plane_method

    Cutting planes were proposed by Ralph Gomory in the 1950s as a method for solving integer programming and mixed-integer programming problems. However, most experts, including Gomory himself, considered them to be impractical due to numerical instability, as well as ineffective because many rounds of cuts were needed to make progress towards the solution.