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  2. Penalty method - Wikipedia

    en.wikipedia.org/wiki/Penalty_method

    In each iteration of the method, we increase the penalty coefficient (e.g. by a factor of 10), solve the unconstrained problem and use the solution as the initial guess for the next iteration. Solutions of the successive unconstrained problems will asymptotically converge to the solution of the original constrained problem.

  3. Assignment problem - Wikipedia

    en.wikipedia.org/wiki/Assignment_problem

    The most common case is the case in which the graph admits a one-sided-perfect matching (i.e., a matching of size r), and s=r. Unbalanced assignment can be reduced to a balanced assignment. The naive reduction is to add n − r {\displaystyle n-r} new vertices to the smaller part and connect them to the larger part using edges of cost 0.

  4. Interior-point method - Wikipedia

    en.wikipedia.org/wiki/Interior-point_method

    An interior point method was discovered by Soviet mathematician I. I. Dikin in 1967. [1] The method was reinvented in the U.S. in the mid-1980s. In 1984, Narendra Karmarkar developed a method for linear programming called Karmarkar's algorithm, [2] which runs in provably polynomial time (() operations on L-bit numbers, where n is the number of variables and constants), and is also very ...

  5. Generalized assignment problem - Wikipedia

    en.wikipedia.org/wiki/Generalized_assignment_problem

    Moreover, the size of each task might vary from one agent to the other. This problem in its most general form is as follows: There are a number of agents and a number of tasks. Any agent can be assigned to perform any task, incurring some cost and profit that may vary depending on the agent-task assignment.

  6. Travelling salesman problem - Wikipedia

    en.wikipedia.org/wiki/Travelling_salesman_problem

    Solution of a travelling salesman problem: the black line shows the shortest possible loop that connects every red dot. In the theory of computational complexity, the travelling salesman problem (TSP) asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the ...

  7. Dual linear program - Wikipedia

    en.wikipedia.org/wiki/Dual_linear_program

    The strong duality theorem says that if one of the two problems has an optimal solution, so does the other one and that the bounds given by the weak duality theorem are tight, i.e.: max x c T x = min y b T y. The strong duality theorem is harder to prove; the proofs usually use the weak duality theorem as a sub-routine.

  8. Collatz conjecture - Wikipedia

    en.wikipedia.org/wiki/Collatz_conjecture

    The machine will perform the following three steps on any odd number until only one 1 remains: Append 1 to the (right) end of the number in binary (giving 2n + 1); Add this to the original number by binary addition (giving 2n + 1 + n = 3n + 1); Remove all trailing 0 s (that is, repeatedly divide by 2 until the result is odd).

  9. Basic feasible solution - Wikipedia

    en.wikipedia.org/wiki/Basic_feasible_solution

    Any linear program can be converted into an equational form by adding slack variables. As a preliminary clean-up step, we verify that: The system A x = b {\displaystyle A\mathbf {x} =\mathbf {b} } has at least one solution (otherwise the whole LP has no solution and there is nothing more to do);