Decomposition methods | Linear programming | Stochastic optimization

Benders decomposition

Benders decomposition (or Benders' decomposition) is a technique in mathematical programming that allows the solution of very large linear programming problems that have a special block structure. This block structure often occurs in applications such as stochastic programming as the uncertainty is usually represented with scenarios. The technique is named after Jacques F. Benders. The strategy behind Benders decomposition can be summarized as divide-and-conquer. That is, in Benders decomposition, the variables of the original problem are divided into two subsets so that a first-stage master problem is solved over the first set of variables, and the values for the second set of variables are determined in a second-stage subproblem for a given first-stage solution. If the subproblem determines that the fixed first-stage decisions are in fact infeasible, then so-called Benders cuts are generated and added to the master problem, which is then re-solved until no cuts can be generated. Since Benders decomposition adds new constraints as it progresses towards a solution, the approach is called "row generation". In contrast, Dantzig–Wolfe decomposition uses "column generation". (Wikipedia).

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Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys How to Set Up the Partial Fraction Decomposition. Just setting them up. See my other videos for actual solved problems.

From playlist Partial Fraction Decomposition

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Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Find the Partial Fraction Decomposition 3x/((x + 1)(x^2 + 1))

From playlist Partial Fraction Decomposition

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From playlist Performing Partial Fraction Decomposition

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FortSP | Block matrix | Linear programming | Dantzig–Wolfe decomposition | Stochastic programming | Column generation | Convex set | Recession cone