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  2. Branch and bound - Wikipedia

    en.wikipedia.org/wiki/Branch_and_bound

    The following is the skeleton of a generic branch and bound algorithm for minimizing an arbitrary objective function f. [3] To obtain an actual algorithm from this, one requires a bounding function bound, that computes lower bounds of f on nodes of the search tree, as well as a problem-specific branching rule.

  3. Petrick's method - Wikipedia

    en.wikipedia.org/wiki/Petrick's_method

    In Boolean algebra, Petrick's method [1] (also known as Petrick function [2] or branch-and-bound method) is a technique described by Stanley R. Petrick (1931–2006) [3] [4] in 1956 [5] [6] for determining all minimum sum-of-products solutions from a prime implicant chart. [7]

  4. 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.

  5. Couenne - Wikipedia

    en.wikipedia.org/wiki/Couenne

    Couenne is an implementation of a branch-and-bound where every subproblem is solved by constructing a linear programming relaxation to obtain a lower bound. Branching may occur at both continuous and integer variables, which is necessary in global optimization problems.

  6. Travelling salesman problem - Wikipedia

    en.wikipedia.org/wiki/Travelling_salesman_problem

    Various branch-and-bound algorithms, which can be used to process TSPs containing thousands of cities. Solution of a TSP with 7 cities using a simple Branch and bound algorithm. Note: The number of permutations is much less than Brute force search. Progressive improvement algorithms, which use techniques reminiscent of linear programming. This ...

  7. Constrained optimization - Wikipedia

    en.wikipedia.org/wiki/Constrained_optimization

    This method [6] runs a branch-and-bound algorithm on problems, where is the number of variables. Each such problem is the subproblem obtained by dropping a sequence of variables x 1 , … , x i {\displaystyle x_{1},\ldots ,x_{i}} from the original problem, along with the constraints containing them.

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  9. Cutting stock problem - Wikipedia

    en.wikipedia.org/wiki/Cutting_stock_problem

    A simple lower bound is obtained by dividing the total amount of product by the size of each master roll. The total product required is 1380 x 22 + 1520 x 25 + ... + 2200 x 20 = 407160 mm. Each master roll is 5600 mm, requiring a minimum of 72.7 rolls, which means 73 rolls or more are required.