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We present a faster implementation of the polynomial time primal simplex algorithm due to Orlin [23]. His algorithm requires O(nm min{log(nC), m log n}) pivots and O(n2 m ??n{log nC, m log n}) time. The bottleneck operations in his algorithm are performing the relabeling operations on nodes, selecting entering arcs for pivots, and performing the pivots. We show how to speed up these operations so as to yield an algorithm whose running time is O(nm. log n) per scaling phase. We show how to extend the dynamic-tree data-structure in order to implement these algorithms. The extension may possibly have other applications as well.

This paper develops a potential reduction algorithm for solving a linear-programming problem directly from a "warm start" initial point that is neither feasible nor optimal. The algorithm is of an "interior point" variety that seeks to reduce a single potential function which simultaneously coerces feasibility improvement (Phase I) and objective value improvement (Phase II). The key feature of the algorithm is the ability to specify beforehand the desired balance between infeasibility and nonoptimality in the following sense. Given a prespecified balancing parameter /3 > 0, the algorithm maintains the following Phase I - Phase II "/3-balancing constraint" throughout: (cTx- Z*) < /3TX, where cTx is the objective function, z* is the (unknown) optimal objective value of the linear program, and Tx measures the infeasibility of the current iterate x. This balancing constraint can be used to either emphasize rapid attainment of feasibility (set large) at the possible expense of good objective function values or to emphasize rapid attainment of good objective values (set /3 small) at the possible expense of a lower infeasibility gap. The algorithm exhibits the following advantageous features: (i) the iterate solutions monotonically decrease the infeasibility measure, (ii) the iterate solutions satisy the /3-balancing constraint, (iii) the iterate solutions achieve constant improvement in both Phase I and Phase II in O(n) iterations, (iv) there is always a possibility of finite termination of the Phase I problem, and (v) the algorithm is amenable to acceleration via linesearch of the potential function.

Given graphs G,H, is it possible to find a matching which, when deleted from G, destroys all copies of H? The answer is obvious for some inputs—notably, when G is a large complete graph the answer is “no”—but in general this can be a very difficult question. In this thesis, we study this decision problem when H is a fixed tree or cycle; our aim is to identify those H for which it can be solved efficiently. The H-transverse matching problem, TM(H) for short, asks whether an input graph admits a matching M such that no subgraph of G − M is isomorphic to H. The main goal of this thesis is the following dichotomy. When H is a triangle or one of a few small-diameter trees, there is a polynomial-time algorithm to find an H-transverse matching if one exists. However, TM(H) is NP-complete when H is any longer cycle or a tree of diameter ≥ 4. In addition, we study the restriction of these problems to structured graph classes. / Graduate

Connectivity is a fundamental tool for matroid theorists, which has become increasingly important in the eventual solution of many problems in matroid theory. Loosely speaking, connectivity can be used to help describe a matroid's structure. In this thesis, we prove a series of results that further the knowledge and understanding in the field of matroid connectivity. These results fall into two parts. First, we focus on 3-connected matroids. A chain theorem is a result that proves the existence of an element, or elements, whose deletion or contraction preserves a predetermined connectivity property. We prove a series of chain theorems for 3-connected matroids where, after fixing a basis B, the elements in B are only eligible for contraction, while the elements not in B are only eligible for deletion. Moreover, we prove a splitter theorem, where a 3-connected minor is also preserved, resolving a conjecture posed by Whittle and Williams in 2013. Second, we consider k-connected matroids, where k >= 3. A certain tree, known as a k-tree, can be used to describe the structure of a k-connected matroid. We present an algorithm for constructing a k-tree for a k-connected matroid M. Provided that the rank of a subset of E(M) can be found in unit time, the algorithm runs in time polynomial in |E(M)|. This generalises Oxley and Semple's (2013) polynomial-time algorithm for constructing a 3-tree for a 3-connected matroid.

A flexible workforce or a versatile machine is employed to perform various types of operations. Often these resources are associated with setups. Whenever a worker or machine switches from processing one type of operation to another a setup time may be required although several operations of a same type can be processed in succession after a single setup. The presence of setups gives rise to the problem of choosing batch sizes that are neither too large nor too small. In the last one and a half decade, many researchers have addressed the problem of scheduling with batching. A majority of articles assumes that there is only one type of scarce resource, which is typically machine. Often there can be two scarce resources such as a worker and a machine or a machine and a tool. We propose a resource constrained scheduling model with a single operator and two or more machines. Whenever the operator changes machine, a setup time is required that may be sequence dependent or sequence independent. We consider the two cases of an open shop and a flow shop. In the open shop case, the order in which a job visits the machines is unrestricted. In the flow shop case, every job must visit the machines in the same order. We consider various scheduling objectives. For variable number of machines, many cases are intractable. We discuss some dominance properties that narrow down the search for an optimal schedule. We present a dynamic programming approach which solves a large number of cases. The running time of the dynamic program is polynomial for a fixed number of machines. For the case of two machines, we show that the dominance properties have a nice interpretation. We develop some algorithms and justify their use by establishing running times, comparing the running times with those of the existing algorithms, and testing the performance of the algorithms.

Boolean function f is k-interval if - input vector viewed as n-bit number - f is true for and only for inputs from given (at most) k intervals. Recognition of k-interval fuction given its DNF representation is coNP-hard problem. This thesis shows that for DNFs from a given solvable class (class C of DNFs is solvable if we can for any DNF F ∈ C decide F ≡ 1 in polynomial time and C is closed under partial assignment) and fixed k we can decide whether F represents k-interval function in polynomial time. 1

In dieser Arbeit verbinden wir die Theorie der Quasi-Ordnungen mit der Theorie der Algorithmen einiger kombinatorischer Objekte. Zuerst entwickeln wir die Theorie der Wohl-Quasi-Ordnungen, WQO, im Zusammenhang zur maximalen Komplexität. Dann geben wir ein allgemeines 0-1-Gesetz für erbliche Eigenschaften, das Auswirkungen für die mittlere Komplexität hat. Dieses Ergebnis für mittlere Komplexität wird auf die Klasse der endlichen Graphen, versehen mit der Relation ``induzierter Subgraph'', angewendet. Wir erhalten, daß eine große Klasse von Problemen, welche z.B. Perfektheit umfaßt, Algorithmen mit im Mittel konstanter Laufzeit haben. Dann zeigen wir, indem wir ein Ergebnis von Damaschke für Cographen veralgemeinern, daß die Klassen der endlichen Ordnungen bzw. Graphen mit beschränktem Dekompositionsdurchmesser bzgl. der Relation ``induzierte Subordnung'' bzw. ``induzierter Subgraph'' WQO sind. Dies führt uns zu linearen Erkennungsalgorithmen für alle erblichen Eigenschaften über diesen Objekten. Unser Hauptresultat ist dann, daß die Menge der endlichen partiellen Ordnungen eine Wohl-Quasi-Ordnung bzgl. einer gewissen Relation &leq; , der sogenannten Ketten-Minor-Relation, ist. Um dies zu beweisen, führen wir eine verwandte Relation auf endlichen formalen Sprachen ein, die die gleiche Eigenschaft hat. Als Folgerung erhalten wir, daß jede Eigenschaft, die erblich bzgl. &leq; ist, einen Test in <em>O(|P|^c)</em> Zeit zuläßt, wobei $c$ von der Eigenschaft abhängt. Dieser Test läßt sich leicht parallelisieren. Auf einer parallelen Maschine (\CRCW\ \PRAM) kann er so implementiert werden, daß er konstante Zeit auf <em>O(|P|^c)</em> Prozessoren benötigt.

We consider the concept of local sets of inference rules. Locality is a syntactic condition on rule sets which guarantees that the inference relation defined by those rules is polynomial time decidable. Unfortunately, determining whether a given rule set is local can be difficult. In this paper we define inductive locality, a strengthening of locality. We also give a procedure which can automatically recognize the locality of any inductively local rule set. Inductive locality seems to be more useful that the earlier concept of strong locality. We show that locality, as a property of rule sets, is undecidable in general.

In this work we formalize a new natural objective (or cost) function for bi-clustering - Monochromatic bi-clustering. Our objective function is suitable for detecting meaningful homogenous clusters based on categorical valued input matrices. Such problems have arisen recently in systems biology where researchers have inferred functional classifications of biological agents based on their pairwise interactions. We analyze the computational complexity of the resulting optimization problems. We show that finding optimal solutions is NP-hard and complement this result by introducing a polynomial time approximation algorithm for this bi-clustering task. This is the first positive approximation guarantee for bi-clustering algorithms. We also show that bi-clustering with our objective function can be viewed as a generalization of correlation clustering.