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In mathematical optimization, the revised simplex method is a variant of George Dantzig's simplex method for linear programming. The revised simplex method is mathematically equivalent to the standard simplex method but differs in implementation. Instead of maintaining a tableau which explicitly represents the constraints adjusted to a set of basic variables, it maintains a representation of a basis of the matrix representing the constraints. The matrix-oriented approach allows for greater computational efficiency by enabling sparse matrix operations. ==Problem formulation== For the rest of the discussion, it is assumed that a linear programming problem has been converted into the following standard form: : where . Without loss of generality, it is assumed that the constraint matrix has full row rank and that the problem is feasible, i.e., there is at least one such that . If is rank-deficient, either there are redundant constraints, or the problem is infeasible. Both situations can be handled by a presolve step. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Revised simplex method」の詳細全文を読む スポンサード リンク
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