A new pivot selection rule for the network simplex algorithm

We present a new network simplex pivot selection rule, which we call theminimum ratio pivot rule, and analyze the worst-case complexity of the resulting network simplex algorithm. We consider networks withn nodes,m arcs, integral arc capacities and integral supplies/demands of nodes. We define a {0, 1}-valued penalty for each arc of the network. The minimum ratio pivot rule is to select that eligible arc as the entering arc whose addition to the basis creates a cycle with the minimum cost-to-penalty ratio. We show that the so-defined primal network simplex algorithm solves minimum cost flow problem within O(nΔ) pivots and in O(Δ(m + n logn)) time, whereΔ is any upper bound on the sum of all arc flows in every feasible flow. For assignment and shortest path problems, our algorithm runs in O(n ²) pivots and O(nm +n ² logn) time.     

Finiteness of the quadratic primal simplex method when s-monotone index selection rules are applied

Central European Journal of Operations Research - This paper considers the primal quadratic simplex method for linearly constrained convex quadratic programming problems. Finiteness of the...     

A polynomial time primal network simplex algorithm for minimum cost flows

Developing a polynomial time primal network simplex algorithm for the minimum cost flow problem has been a long standing open problem. In this paper, we develop one such algorithm that runs in O(min(n ²m lognC, n ²m² logn)) time, wheren is the number of nodes in the network,m is the number of arcs, andC denotes the maximum absolute arc costs if arc costs are integer and ∞ otherwise. We first introduce a pseudopolynomial variant of the network simplex algorithm called the “premultiplier algorithm”. We then develop a cost-scaling version of the premultiplier algorithm that solves the minimum cost flow problem in O(min(nm lognC, nm ² logn)) pivots. With certain simple data structures, the average time per pivot can be shown to be O(n). We also show that the diameter of the network polytope is O(nm logn).     

Reoptimization procedures for bounded variable primal simplex network algorithms

Many algorithms and applications call for the use of a network subprogram which must be optimized numerous times with slight changes to the problem data. Bound and right-hand-side changes to an existing basis tree may yield an infeasible basic solution. This report gives a sequence of steps that modify an existing basic solution to reflect such changes and streamline the reoptimization process.     

The s-monotone index selection rules for pivot algorithms of linear programming

In this paper we introduce the concept of s-monotone index selection rule for linear programming problems. We show that several known anti-cycling pivot rules like the minimal index, Last-In–First-Out and the most-often-selected-variable pivot rules are s-monotone index selection rules. Furthermore, we show a possible way to define new s-monotone pivot rules. We prove that several known algorithms like the primal (dual) simplex, MBU-simplex algorithms and criss-cross algorithm with s-monotone pivot rules are finite methods.     

Computational aspects of simplex and MBU-simplex algorithms using different anti-cycling pivot rules

Abstract

Several variations of index selection rules for simplex-type algorithms for linear programming, like the Last-In-First-Out or the Most-Often-Selected-Variable are rules not only theoretically finite, but also provide significant flexibility in choosing a pivot element. Based on an implementation of the primal simplex and the monotonic build-up (MBU) simplex method, the practical benefit of the flexibility of these anti-cycling pivot rules is evaluated using public benchmark LP test sets. Our results also provide numerical evidence that the MBU-simplex algorithm is a viable alternative to the traditional simplex algorithm.     

A largest-distance pivot rule for the simplex algorithm

We propose a new pivot rule for the simplex algorithm, which is demonstrative in the dual space intuitively. Although it is based on normalized reduced costs, like the steepest-edge rule and its variants, the rule is much simpler and cheaper than the latter. We report computational results obtained with the 47 largest Netlib problems in terms of the number of rows and columns, all of the 16 Kennington problems, and the 17 largest BPMPD problems. Over the total 80 problems, a variant of the rule outperformed the Devex rule with iterations and time ratio 1.43 and 3.24, respectively.     

Optimal pivot path of the simplex method for linear programming based on reinforcement learning

Based on the existing pivot rules, the simplex method for linear programming is not polynomial in the worst case. Therefore, the optimal pivot of the simplex method is crucial. In this paper, we propose the optimal rule to find all the shortest pivot paths of the simplex method for linear programming problems based on Monte Carlo tree search. Specifically, we first propose the SimplexPseudoTree to transfer the simplex method into tree search mode while avoiding repeated basis variables. Secondly...     

A note on primal network flow algorithms

Klein [1967] andDomschke [1973] have developed primal algorithms for network flow problems. An alternative derivation shows that these algorithms implicitly take advantage of duality and end up with an optimal dual solution.

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Zusammenfassung Ausgangspunkt dieser Arbeit sind die Algorithmen vonKlein [1967] undDomschke [1973] zur Bestimmung kostenminimaler Flüsse in Netzwerken. Es werden allgemein interessierende Zusammenhänge zwischen primalen und primal-dualen Algorithmen aufgezeigt.

     

A computational comparison of the dinic and network simplex methods for maximum flow

We study the implementation of two fundamentally different algorithms for solving the maximum flow problem: Dinic's method and the network simplex method. For the former, we present the design of a storage-efficient implementation. For the latter, we develop a "steepest-edge" pivot selection criterion that is easy to include in an existing network simplex implementation. We compare the computational efficiency of these two methods on a personal computer with a set of generated problems of up to 4 600 nodes and 27 000 arcs.This research was supported in part by the National Science Foundation under Grant Nos. MCS-8113503 and DMS-8512277.     

A network simplex method

Simple combinatorial modifications are given which ensure finiteness in the primal simplex method for the transshipment problem and the upper-bounded primal simplex method for the minimum cost flow problem. The modifications involve keeping strongly feasible bases. An efficient algorithm is given for converting any feasible basis into a strongly feasible basis. Strong feasibility is preserved by a rule for choosing the leaving basic variable at each simplex iteration. The method presented is closely related to a new perturbation technique and to previously known degeneracy modifications for shortest path problems and maximum flow problems.The author holds a National Research Council of Canada Post-Doctorate Fellowship.     

Experimental investigations in combining primal dual interior point method and simplex based LP solvers

The use of a primal dual interior point method (PD) based optimizer as a robust linear programming (LP) solver is now well established. Instead of replacing the sparse simplex algorithm (SSX), the PD is increasingly seen as complementing it. The progress of PD iterations is not hindered by the degeneracy or stalling problem of SSX, indeed it reaches the near optimum solution very quickly. The SSX algorithm, in contrast, is not affected by the numeral instabilities which slow down the convergence of the PD near the optimal face. If the solution to the LP problem is non-unique, the PD algorithm converges to an interior point of the solution set while the SSX algorithm finds an extreme point solution. To take advantage of the attractive properties of both the PD and the SSX, we have designed a hybrid framework whereby crossover from PD to SSX can take place at any stage of the PD optimization run. The crossover to SSX involves the partition of the PD solution set to active and dormant variables. In this paper we examine the practical difficulties in partitioning the solution set, we discuss the reliability of predicting the solution set partition before optimality is reached and report the results of combining exact and inexact prediction with SSX basis recovery.     

Stochastic dominance based comparison for system selection

We present two complementing selection procedures for comparing simulated systems based on the stochastic dominance relationship of a performance metric of interest. The decision maker specifies an output quantile set representing a section of the distribution of the metric, e.g., downside or upside risks or central tendencies, as the basis for comparison. The first procedure compares systems over the quantile set of interest by a first-order stochastic dominance criterion. The systems that are deemed nondominant in the first procedure could be compared by a weaker almost first-order stochastic dominance criterion in the second procedure. Numerical examples illustrate the capabilities of the proposed procedures.     

A comparison of three linear rules for classification

This paper compares three different linear procedures for classification: the normal one, the canonical one and a distribution-free one recently described by Heuchenne. The study is mainly conducted using a simulation which makes it possible to compute the probabilities of correct allocation of the three methods in 3888 different cases. The normal rule looks slightly better than Heuchenne's, which looks clearly better than the canonical one. Finally, inference on Heuchenne's method is examined and conditions under which this method is optimal are given.     

A primal all-integer algorithm based on irreducible solutions

 This paper introduces an exact primal augmentation algorithm for solving general linear integer programs. The algorithm iteratively substitutes one column in a tableau by other columns that correspond to irreducible solutions of certain linear diophantine inequalities. We prove that various versions of our algorithm are finite. It is a major concern in this paper to show how the subproblem of replacing a column can be accomplished effectively. An implementation of the presented algorithms is given. Computational results for a number of hard 0/1 integer programs from the MIPLIB demonstrate the practical power of the method. Received: April 23, 2001 / Accepted: May 2002 Published online: March 21, 2003 RID="*" ID="*" Supported by grants FKZ 0037KD0099 and FKZ 2495A/0028G of the Kultusministerium of Sachsen-Anhalt. RID="*" ID="*" Supported by grants FKZ 0037KD0099 and FKZ 2495A/0028G of the Kultusministerium of Sachsen-Anhalt. RID="*" ID="*" Supported by grants FKZ 0037KD0099 and FKZ 2495A/0028G of the Kultusministerium of Sachsen-Anhalt. RID="#" ID="#"Supported by a Gerhard-Hess-Preis and grant WE 1462 of the Deutsche Forschungsgemeinschaft, and by the European DONET program TMR ERB FMRX-CT98-0202. Mathematics Subject Classification (1991): 90C10     

A network simplex based algorithm for the minimum cost proportional flow problem with disconnected subnetworks

In this study, we present a variant of the minimum cost network flow problem where the associated graph contains several disconnected subgraphs and it is required that the flows on arcs belonging to same arc subsets to be proportional. This type of network is mostly observed in large supply chains of assemble-to-order products. It is shown that any feasible solution of a reformulation of this problem has a special characteristic. By taking into account this fact, a network simplex based primal simplex algorithm is developed and its details are provided.     

A finiteness theorem for primal extensions

A set W⊆V(G) is called homogeneous in a graph G if 2?|W|?|V(G)|-1, and N(x)?W=N(y)?W for each x,y∈W. A graph without homogeneous sets is called prime. A graph H is called a (primal) extension of a graph G if G is an induced subgraph of H, and H is a prime graph. An extension H of G is minimal if there are no extensions of G in the set ISub(H)?{H}. We denote by Ext(G) the set of all minimal extensions of a graph G.We investigate the following problem: find conditions under which Ext(G) is a finite set. The main result of Giakoumakis (Discrete Math. 177 (1997) 83-97) is the following sufficient condition.

Theorem. If every homogeneous set of G has exactly two vertices thenExt(G)is a finite set.     

Comparison of selection rules for ordinal optimization

The evaluation of performance of a design for complex discrete event systems through simulation is usually very time consuming. Optimizing the system performance becomes even more computationally infeasible. Ordinal optimization (OO) is a technique introduced to attack this difficulty in system design by looking at “order” in performances among designs instead of “value” and providing a probability guarantee for a good enough solution instead of the best for sure. The selection rule, known as the rule to decide which subset of designs to select as the OO solution, is a key step in applying the OO method. Pairwise elimination and round robin comparison are two selection rule examples. Many other selection rules are also frequently used in the ordinal optimization literature. To compare selection rules, we first identify some general facts about selection rules. Then we use regression functions to quantify the efficiency of a group of selection rules, including some frequently used rules. A procedure to predict good selection rules is proposed and verified by simulation and by examples. Selection rules that work well most of the time are recommended.     

A distance-based comparison of basic voting rules

In this paper we provide a comparison of different voting rules in a distance-based framework with the help of computer simulations. Taking into account the informational requirements to operate such voting rules and the outcomes of two well-known reference rules, we identify the Copeland rule as a good compromise between these two reference rules. It will be shown that the outcome of the Copeland rule is “close” to the outcomes of the reference rules, but it requires less informational input and has lower computational complexity.