Branching strategies for mixed-integer programs containing logical constraints and decomposable structure

Decision-making optimisation problems can include discrete selections, e.g. selecting a route, arranging non-overlapping items or designing a network of items. Branch-and-bound (B&B), a widely applied divide-and-conquer framework, often solves such problems by considering a continuous approximation, e.g. replacing discrete variable domains by a continuous superset. Such approximations weaken the logical relations, e.g. for discrete variables corresponding to Boolean variables. Branching in B&B reintroduces logical relations by dividing the search space. This thesis studies designing B&B branching strategies, i.e. how to divide the search space, for optimisation problems that contain both a logical and a continuous structure. We begin our study with a large-scale, industrially-relevant optimisation problem where the objective consists of machine-learnt gradient-boosted trees (GBTs) and convex penalty functions. GBT functions contain if-then queries which introduces a logical structure to this problem. We propose decomposition-based rigorous bounding strategies and an iterative heuristic that can be embedded into a B&B algorithm. We approach branching with two strategies: a pseudocost initialisation and strong branching that target the structure of GBT and convex penalty aspects of the optimisation objective, respectively. Computational tests show that our B&B approach outperforms state-of-the-art solvers in deriving rigorous bounds on optimality. Our second project investigates how satisfiability modulo theories (SMT) derived unsatisfiable cores may be utilised in a B&B context. Unsatisfiable cores are subsets of constraints that explain an infeasible result. We study two-dimensional bin packing (2BP) and develop a B&B algorithm that branches on SMT unsatisfiable cores. We use the unsatisfiable cores to derive cuts that break 2BP symmetries. Computational results show that our B&B algorithm solves 20% more instances when compared with commercial solvers on the tested instances. Finally, we study convex generalized disjunctive programming (GDP), a framework that supports logical variables and operators. Convex GDP includes disjunctions of mathematical constraints, which motivate branching by partitioning the disjunctions. We investigate separation by branching, i.e. eliminating solutions that prevent rigorous bound improvement, and propose a greedy algorithm for building the branches. We propose three scoring methods for selecting the next branching disjunction. We also analyse how to leverage infeasibility to expedite the B&B search. Computational results show that our scoring methods can reduce the number of explored B&B nodes by an order of magnitude when compared with scoring methods proposed in literature. Our infeasibility analysis further reduces the number of explored nodes.Open Acces

Clifford Algebra: A Case for Geometric and Ontological Unification

Robert Batterman’s ontological insights (2002, 2004, 2005) are apt: Nature abhors singularities. “So should we,” responds the physicist. However, the epistemic assessments of Batterman concerning the matter prove to be less clear, for in the same vein he write that singularities play an essential role in certain classes of physical theories referring to certain types of critical phenomena. I devise a procedure (“methodological fundamentalism”) which exhibits how singularities, at least in principle, may be avoided within the same classes of formalisms discussed by Batterman. I show that we need not accept some divergence between explanation and reduction (Batterman 2002), or between epistemological and ontological fundamentalism (Batterman 2004, 2005). Though I remain sympathetic to the ‘principle of charity’ (Frisch (2005)), which appears to favor a pluralist outlook, I nevertheless call into question some of the forms such pluralist implications take in Robert Batterman’s conclusions. It is difficult to reconcile some of the pluralist assessments that he and some of his contemporaries advocate with what appears to be a countervailing trend in a burgeoning research tradition known as Clifford (or geometric) algebra. In my critical chapters (2 and 3) I use some of the demonstrated formal unity of Clifford algebra to argue that Batterman (2002) equivocates a physical theory’s ontology with its purely mathematical content. Carefully distinguishing the two, and employing Clifford algebraic methods reveals a symmetry between reduction and explanation that Batterman overlooks. I refine this point by indicating that geometric algebraic methods are an active area of research in computational fluid dynamics, and applied in modeling the behavior of droplet-formation appear to instantiate a “methodologically fundamental” approach. I argue in my introductory and concluding chapters that the model of inter-theoretic reduction and explanation offered by Fritz Rohrlich (1988, 1994) provides the best framework for accommodating the burgeoning pluralism in philosophical studies of physics, with the presumed claims of formal unification demonstrated by physicists choices of mathematical formalisms such as Clifford algebra. I show how Batterman’s insights can be reconstructed in Rohrlich’s framework, preserving Batterman’s important philosophical work, minus what I consider are his incorrect conclusions

Educating and training mathematics teachers for secondary schools in Ireland: a new perspective on teacher education

This thesis is a record of experiments in the education of mathematics teachers for Irish Secondary schools conducted at Thomond College of Education, Limerick during the years 1975–77 inclusive. But it is more than a mere record of successes and failures. In its analyses and syntheses, based on experiments and programmes conducted under actual conditions, it endeavours in a true spirit of research in mathematical education to provide new insights. The research culminates in the redefinition of an old problem in mathematical education, and a first step towards a viable solution to the redefined problem is presented

Quantum correlations: a window into fundamental physics

The past century has seen many of nature's secrets unravelled by the immensely successful theories of particle physics and general relativity, frameworks in which the world is described as a collection of many quantum fields, lying on a background classical spacetime. High-energy signals, originating naturally from the cosmos, or artificially from particle accelerators, held many empirical clues in support of these descriptions. In recent years, the formidable advances in quantum control have brought to light a model-agnostic conception of physics, once thought to be merely philosophical, as an alternative path of fundamental investigations. This modern information theoretic framework, eschews any description of nature beyond the correlations between measurements predicted by quantum theory. In this thesis, three questions of fundamental physics are studied from the perspective of quantum information and quantum control.Open Acces

On the mechanisation of the logic of partial functions

PhD ThesisIt is well known that partial functions arise frequently in formal reasoning about programs. A partial function may not yield a value for every member of its domain. Terms that apply partial functions thus may not denote, and coping with such terms is problematic in two-valued classical logic. A question is raised: how can reasoning about logical formulae that can contain references to terms that may fail to denote (partial terms) be conducted formally? Over the years a number of approaches to coping with partial terms have been documented. Some of these approaches attempt to stay within the realm of two-valued classical logic, while others are based on non-classical logics. However, as yet there is no consensus on which approach is the best one to use. A comparison of numerous approaches to coping with partial terms is presented based upon formal semantic definitions. One approach to coping with partial terms that has received attention over the years is the Logic of Partial Functions (LPF), which is the logic underlying the Vienna Development Method. LPF is a non-classical three-valued logic designed to cope with partial terms, where both terms and propositions may fail to denote. As opposed to using concrete undfined values, undefinedness is treated as a \gap", that is, the absence of a defined value. LPF is based upon Strong Kleene logic, where the interpretations of the logical operators are extended to cope with truth value \gaps". Over the years a large body of research and engineering has gone into the development of proof based tool support for two-valued classical logic. This has created a major obstacle that affects the adoption of LPF, since such proof support cannot be carried over directly to LPF. Presently, there is a lack of direct proof support for LPF. An aim of this work is to investigate the applicability of mechanised (automated) proof support for reasoning about logical formulae that can contain references to partial terms in LPF. The focus of the investigation is on the basic but fundamental two-valued classical logic proof procedure: resolution and the associated technique proof by contradiction. Advanced proof techniques are built on the foundation that is provided by these basic fundamental proof techniques. Looking at the impact of these basic fundamental proof techniques in LPF is thus the essential and obvious starting point for investigating proof support for LPF. The work highlights the issues that arise when applying these basic techniques in LPF, and investigates the extent of the modifications needed to carry them over to LPF. This work provides the essential foundation on which to facilitate research into the modification of advanced proof techniques for LPF.EPSR

Students´ language in computer-assisted tutoring of mathematical proofs

Truth and proof are central to mathematics. Proving (or disproving) seemingly simple statements often turns out to be one of the hardest mathematical tasks. Yet, doing proofs is rarely taught in the classroom. Studies on cognitive difficulties in learning to do proofs have shown that pupils and students not only often do not understand or cannot apply basic formal reasoning techniques and do not know how to use formal mathematical language, but, at a far more fundamental level, they also do not understand what it means to prove a statement or even do not see the purpose of proof at all. Since insight into the importance of proof and doing proofs as such cannot be learnt other than by practice, learning support through individualised tutoring is in demand. This volume presents a part of an interdisciplinary project, set at the intersection of pedagogical science, artificial intelligence, and (computational) linguistics, which investigated issues involved in provisioning computer-based tutoring of mathematical proofs through dialogue in natural language. The ultimate goal in this context, addressing the above-mentioned need for learning support, is to build intelligent automated tutoring systems for mathematical proofs. The research presented here has been focused on the language that students use while interacting with such a system: its linguistic propeties and computational modelling. Contribution is made at three levels: first, an analysis of language phenomena found in students´ input to a (simulated) proof tutoring system is conducted and the variety of students´ verbalisations is quantitatively assessed, second, a general computational processing strategy for informal mathematical language and methods of modelling prominent language phenomena are proposed, and third, the prospects for natural language as an input modality for proof tutoring systems is evaluated based on collected corpora