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Dual-simplex invariant and Farkas infeasibility certificate
Statement
Let 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 with and a column with minimizing preserves dual feasibility. If there is no nonbasic with , then satisfies and , so 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 , a dual-feasible multiplier , and the dual-simplex row and ratio choices.
Basis dictionaries and reduced costs for revised simplex: For every column , its reduced cost is .
Dual revised simplex with a finite pivot budget: Among with , choose one minimizing , with least variable index among ties.
Revised simplex standard-form linear program: A vector satisfying and is called a Farkas infeasibility certificate for this sign convention.
Proof
Fix an eligible pivot and put . With , the vector satisfies , so the entering column has new reduced cost zero. The leaving basic column has , hence its new reduced cost is .
If no nonbasic column has , then : the basic columns give the coordinates of , and every nonbasic column has nonnegative th transformed entry. Also . By [F3], is a Farkas infeasibility certificate; any feasible would give , a contradiction.
For any other nonbasic , write . Its new reduced cost is . If , this is nonnegative because . If , the ratio rule gives , so . Thus every new nonbasic reduced cost is nonnegative.
If the basis is primal feasible, its basic vector and dual-feasible satisfy , because . For every feasible , and imply , so is optimal. The algorithm's budget-exhausted branch asserts no further mathematical conclusion.
Depends on
Used by
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Sources
- Dimitris Bertsimas and John N. Tsitsiklis, Introduction to Linear Optimization, §§4.3 and 4.5–4.6 (standard reference, not scraped)
- Michel Goemans, MIT 18.310 Lecture Notes: Linear Programming, §§2.4–3 (standard reference, not scraped)