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A Survey on Pivot Rules for Linear Programming
 ANNALS OF OPERATIONS RESEARCH. (SUBMITTED
, 1991
"... The purpose of this paper is to survey the various pivot rules of the simplex method or its variants that have been developed in the last two decades, starting from the appearance of the minimal index rule of Bland. We are mainly concerned with the finiteness property of simplex type pivot rules. Th ..."
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Cited by 10 (1 self)
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The purpose of this paper is to survey the various pivot rules of the simplex method or its variants that have been developed in the last two decades, starting from the appearance of the minimal index rule of Bland. We are mainly concerned with the finiteness property of simplex type pivot rules. There are some other important topics in linear programming, e.g. complexity theory or implementations, that are not included in the scope of this paper. We do not discuss ellipsoid methods nor interior point methods. Well known classical results concerning the simplex method are also not particularly discussed in this survey, but the connection between the new methods and the classical ones are discussed if there is any. In this paper we discuss three classes of recently developed pivot rules for linear programming. The first class (the largest one) of the pivot rules we discuss is the class of essentially combinatorial pivot rules. Namely these rules only use labeling and signs of the variab...
A Monotonic BuildUp Simplex Algorithm for Linear Programming
, 1991
"... We devise a new simplex pivot rule which has interesting theoretical properties. Beginning with a basic feasible solution, and any nonbasic variable having a negative reduced cost, the pivot rule produces a sequence of pivots such that ultimately the originally chosen nonbasic variable enters the ba ..."
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Cited by 5 (1 self)
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We devise a new simplex pivot rule which has interesting theoretical properties. Beginning with a basic feasible solution, and any nonbasic variable having a negative reduced cost, the pivot rule produces a sequence of pivots such that ultimately the originally chosen nonbasic variable enters the basis, and all reduced costs which were originally nonnegative remain nonnegative. The pivot rule thus monotonically builds up to a dual feasible, and hence optimal, basis. A surprising property of the pivot rule is that the pivot sequence results in intermediate bases which are neither primal nor dual feasible. We prove correctness of the procedure, give a geometric interpretation, and relate it to other pivoting rules for linear programming.
LEASTINDEX ANTICYCLING RULES, LindAcR
, 1998
"... this paper. leastindex rules were designed for network flow problems, linear optimization problems, linear complementarity problems and oriented matroid programming problems. These classes will be considered in the sequel. ..."
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this paper. leastindex rules were designed for network flow problems, linear optimization problems, linear complementarity problems and oriented matroid programming problems. These classes will be considered in the sequel.
1 CONSTRUCTIVE PROOF OF FARKAS ’ LEMMA IMPERVIOUS TO DEGENERACY
, 2003
"... and Hanson does indeed constitute by itself a new constructive proof of Farkas’ lemma (in primal and dual form), we apply it to solve the restricted feasibility subproblems that occur in two primaldual activeset linear programming algorithms, thus establishing the relationship with a recently pro ..."
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and Hanson does indeed constitute by itself a new constructive proof of Farkas’ lemma (in primal and dual form), we apply it to solve the restricted feasibility subproblems that occur in two primaldual activeset linear programming algorithms, thus establishing the relationship with a recently proposed (dated in October, 2002) primaldual activeset scheme. As a result, the proposed algorithms are impervious to degeneracy, both in Phase I and in Phase II, and finite in exact arithmetic without having to resort to any anticycling pivoting rule, and hence they are by themselves constructive proofs of the strong duality theorem. Finally, we describe the sparse implementation of both algorithms in terms of certain sparse Cholesky factorization.