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Dual-simplex invariant and Farkas infeasibility certificate

Statement

Let B be a basis of the minimization problem for which all nonbasic reduced costs are nonnegative. At an iteration of Dual revised simplex with a finite pivot budget, a pivot chosen from a row i with xBi<0 and a column j with dij=(AB−1aj)i<0 minimizing cˉj/(−dij) preserves dual feasibility. If there is no nonbasic j with dij<0, then q=AB−Tei satisfies ATq≥0 and bTq<0, so q is a Farkas infeasibility certificate as defined in Revised simplex standard-form linear program and the primal problem is infeasible. If the current basis is both primal and dual feasible, it is optimal. A finite pivot-budget exhaustion is inconclusive and does not assert infeasibility or convergence.

Facts & Assumptions

Given: Exact standard-form data, a basis B, a dual-feasible multiplier y=AB−TcB, and the dual-simplex row and ratio choices.

[F1]

Basis dictionaries and reduced costs for revised simplex: For every column j, its reduced cost is cˉj=cj−ajTy(B).

[F2]

Dual revised simplex with a finite pivot budget: Among j with dij<0, choose one minimizing cˉj/(−dij), with least variable index among ties.

[F3]

Revised simplex standard-form linear program: A vector q∈Rm satisfying ATq≥0 and bTq<0 is called a Farkas infeasibility certificate for this sign convention.

Proof

1.1F1F2algebra

Fix an eligible pivot (i,j) and put q=AB−Tei. With α=cˉj/dij≤0, the vector y′=y+αq satisfies ajTy′=cj, so the entering column has new reduced cost zero. The leaving basic column aBi has qTaBi=1, hence its new reduced cost is −α≥0.

1.2F1F2F3algebra

If no nonbasic column has dij<0, then ATq≥0: the basic columns give the coordinates of ei, and every nonbasic column has nonnegative ith transformed entry. Also bTq=eiTAB−1b=xBi<0. By [F3], q is a Farkas infeasibility certificate; any feasible x≥0 would give bTq=xTATq≥0, a contradiction.

2.1F1F2step 1.1algebra

For any other nonbasic k, write dik=qTak. Its new reduced cost is cˉk′=cˉk−αdik. If dik≥0, this is nonnegative because α≤0. If dik<0, the ratio rule gives cˉj/(−dij)≤cˉk/(−dik), so cˉk′=(−dik)(cˉk/(−dik)−cˉj/(−dij))≥0. Thus every new nonbasic reduced cost is nonnegative.

3.1F1F2algebra∎

If the basis is primal feasible, its basic vector x and dual-feasible y satisfy cTx=bTy, because cBTAB−1b=yTb. For every feasible z, ATy≤c and z≥0 imply cTz≥bTy=cTx, so x is optimal. The algorithm's budget-exhausted branch asserts no further mathematical conclusion.

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