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  2. Travelling salesman problem - Wikipedia

    en.wikipedia.org/wiki/Travelling_salesman_problem

    For example, the minimum spanning tree of the graph associated with an instance of the Euclidean TSP is a Euclidean minimum spanning tree, and so can be computed in expected O(n log n) time for n points (considerably less than the number of edges). This enables the simple 2-approximation algorithm for TSP with triangle inequality above to ...

  3. Concorde TSP Solver - Wikipedia

    en.wikipedia.org/wiki/Concorde_TSP_Solver

    The Concorde TSP Solver is a program for solving the travelling salesman problem. It was written by David Applegate , Robert E. Bixby , Vašek Chvátal , and William J. Cook , in ANSI C , and is freely available for academic use.

  4. Greedy algorithm - Wikipedia

    en.wikipedia.org/wiki/Greedy_algorithm

    The matching pursuit is an example of a greedy algorithm applied on signal approximation. A greedy algorithm finds the optimal solution to Malfatti's problem of finding three disjoint circles within a given triangle that maximize the total area of the circles; it is conjectured that the same greedy algorithm is optimal for any number of circles.

  5. Christofides algorithm - Wikipedia

    en.wikipedia.org/wiki/Christofides_algorithm

    The cost of the solution produced by the algorithm is within 3/2 of the optimum. To prove this, let C be the optimal traveling salesman tour. Removing an edge from C produces a spanning tree, which must have weight at least that of the minimum spanning tree, implying that w(T) ≤ w(C) - lower bound to the cost of the optimal solution.

  6. NP (complexity) - Wikipedia

    en.wikipedia.org/wiki/NP_(complexity)

    The above example can be generalized for any decision problem. Given any instance I of problem Π {\displaystyle \Pi } and witness W, if there exists a verifier V so that given the ordered pair (I, W) as input, V returns "yes" in polynomial time if the witness proves that the answer is "yes" or "no" in polynomial time otherwise, then Π ...

  7. Dynamic programming - Wikipedia

    en.wikipedia.org/wiki/Dynamic_programming

    The word dynamic was chosen by Bellman to capture the time-varying aspect of the problems, and because it sounded impressive. [12] The word programming referred to the use of the method to find an optimal program, in the sense of a military schedule for training or logistics.

  8. Job-shop scheduling - Wikipedia

    en.wikipedia.org/wiki/Job-shop_scheduling

    Since the traveling salesman problem is NP-hard, the job-shop problem with sequence-dependent setup is clearly also NP-hard since the TSP is a special case of the JSP with a single job (the cities are the machines and the salesman is the job). [citation needed]

  9. Vehicle routing problem - Wikipedia

    en.wikipedia.org/wiki/Vehicle_routing_problem

    There are many methods to solve vehicle routing problems manually. For example, optimum routing is a big efficiency issue for forklifts in large warehouses. Some of the manual methods to decide upon the most efficient route are: Largest gap, S-shape, Aisle-by-aisle, Combined and Combined +.