Decomposition Principle for Linear Programs
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Additional excerpts
...…. . . . . . . . . . . . 31 6.3 General ℓ1 regularized loss minimization . . . . . . . . . . . . . . . . . . . . 32 6.4 Lasso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 6.5 Sparse inverse covariance selection . . . . . . . . . . . . . . . . . . . . . . . . 33...
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Cites background from "Decomposition Principle for Linear ..."
...Some classic and important modern references include those by Dantzig and Wolfe [66], Benders [23], Lasdon [118], Geoffrion [93], Tsitsiklis [189], Bertsekas and Tsitsklis [27], Censor and Zenios [50], and Nedic̀ and Ozdaglar [144, 145]....
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2,782 citations
Cites methods from "Decomposition Principle for Linear ..."
...We assume the reader to be familiar with the theory of convex polyhedral sets and with the computational aspects of solving a linear programming problem by the simplex method; see e.g. TUCKER [13], GOLDMAN [8] and GASS [ 6 ]....
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