Decomposition and reformulation of integer linear programming problems

This thesis deals with an investigation of Decomposition and Reformulation to solve Integer Linear Programming Problems. This method is often a very successful approach computationally, producing high-quality solutions for well-structured combinatorial optimization problems like vehicle routing, cutting stock, p-median and generalized assignment . However, until now the method has always been tailored to the specific problem under investigation. The principal innovation of this thesis is to develop a new framework able to apply this concept to a generic MIP problem. The new approach is thus capable of auto-decomposition and autoreformulation of the input problem applicable as a resolving black box algorithm and works as a complement and alternative to the normal resolving techniques. The idea of Decomposing and Reformulating (usually called in literature Dantzig and Wolfe Decomposition DWD) is, given a MIP, to convexify one (or more) subset(s) of constraints (slaves) and working on the partially convexified polyhedron(s) obtained. For a given MIP several decompositions can be defined depending from what sets of constraints we want to convexify. In this thesis we mainly reformulate MIPs using two sets of variables: the original variables and the extended variables (representing the exponential extreme points). The master constraints consist of the original constraints not included in any slaves plus the convexity constraint(s) and the linking constraints(ensuring that each original variable can be viewed as linear combination of extreme points of the slaves). The solution procedure consists of iteratively solving the reformulated MIP (master) and checking (pricing) if a variable of reduced costs exists, and in which case adding it to the master and solving it again (columns generation), or otherwise stopping the procedure. The advantage of using DWD is that the reformulated relaxation gives bounds stronger than the original LP relaxation, in addition it can be incorporated in a Branch and bound scheme (Branch and Price) in order to solve the problem to optimality. If the computational time for the pricing problem is reasonable this leads in practice to a stronger speed up in the solution time, specially when the convex hull of the slaves is easy to compute, usually because of its special structure.

Hybrid constraint programming and metaheuristic methods for large scale optimization problems

This work presents hybrid Constraint Programming (CP) and metaheuristic methods for the solution of Large Scale Optimization Problems; it aims at integrating concepts and mechanisms from the metaheuristic methods to a CP-based tree search environment in order to exploit the advantages of both approaches. The modeling and solution of large scale combinatorial optimization problem is a topic which has arisen the interest of many researcherers in the Operations Research field; combinatorial optimization problems are widely spread in everyday life and the need of solving difficult problems is more and more urgent. Metaheuristic techniques have been developed in the last decades to effectively handle the approximate solution of combinatorial optimization problems; we will examine metaheuristics in detail, focusing on the common aspects of different techniques. Each metaheuristic approach possesses its own peculiarities in designing and guiding the solution process; our work aims at recognizing components which can be extracted from metaheuristic methods and re-used in different contexts. In particular we focus on the possibility of porting metaheuristic elements to constraint programming based environments, as constraint programming is able to deal with feasibility issues of optimization problems in a very effective manner. Moreover, CP offers a general paradigm which allows to easily model any type of problem and solve it with a problem-independent framework, differently from local search and metaheuristic methods which are highly problem specific. In this work we describe the implementation of the Local Branching framework, originally developed for Mixed Integer Programming, in a CP-based environment. Constraint programming specific features are used to ease the search process, still mantaining an absolute generality of the approach. We also propose a search strategy called Sliced Neighborhood Search, SNS, that iteratively explores slices of large neighborhoods of an incumbent solution by performing CP-based tree search and encloses concepts from metaheuristic techniques. SNS can be used as a stand alone search strategy, but it can alternatively be embedded in existing strategies as intensification and diversification mechanism. In particular we show its integration within the CP-based local branching. We provide an extensive experimental evaluation of the proposed approaches on instances of the Asymmetric Traveling Salesman Problem and of the Asymmetric Traveling Salesman Problem with Time Windows. The proposed approaches achieve good results on practical size problem, thus demonstrating the benefit of integrating metaheuristic concepts in CP-based frameworks.

ROP Gadgets hiding techniques in Open Source Projects

Today there are many techniques that allows to exploit vulnerabilities of an application; there are also many techniques that are designed to stop these exploit attacks. This thesis wants to highlight how a specific type of attack, based on a technique called Return Oriented Programming (ROP), can be easily applied to binaries with particular characteristics. A new method that allows the injection of "useful" code in an Open Source projects without arousing suspicions is presented; this is possible because of the harmless aspects of the injected code. This useful code facilitate a ROP attack against an executable that contains vulnerable bugs. The injection process can be visualized in environment where an user can contribute with own code to a particular Open Source project. This thesis also highlights how current software protections are not correctly applied to Open Source project, thus enabling the proposed approach.

Portfolio Approaches in Constraint Programming

Recent research has shown that the performance of a single, arbitrarily efficient algorithm can be significantly outperformed by using a portfolio of —possibly on-average slower— algorithms. Within the Constraint Programming (CP) context, a portfolio solver can be seen as a particular constraint solver that exploits the synergy between the constituent solvers of its portfolio for predicting which is (or which are) the best solver(s) to run for solving a new, unseen instance. In this thesis we examine the benefits of portfolio solvers in CP. Despite portfolio approaches have been extensively studied for Boolean Satisfiability (SAT) problems, in the more general CP field these techniques have been only marginally studied and used. We conducted this work through the investigation, the analysis and the construction of several portfolio approaches for solving both satisfaction and optimization problems. We focused in particular on sequential approaches, i.e., single-threaded portfolio solvers always running on the same core. We started from a first empirical evaluation on portfolio approaches for solving Constraint Satisfaction Problems (CSPs), and then we improved on it by introducing new data, solvers, features, algorithms, and tools. Afterwards, we addressed the more general Constraint Optimization Problems (COPs) by implementing and testing a number of models for dealing with COP portfolio solvers. Finally, we have come full circle by developing sunny-cp: a sequential CP portfolio solver that turned out to be competitive also in the MiniZinc Challenge, the reference competition for CP solvers.

Using Dynamic Programming Decomposition for Revenue Management with Opaque Products

Opaque products enable service providers to hide specific characteristics of their service fulfillment from the customer until after purchase. Prominent examples include internet-based service providers selling airline tickets without defining details, such as departure time or operating airline, until the booking has been made. Owing to the resulting flexibility in resource utilization, the traditional revenue management process needs to be modified. In this paper, we extend dynamic programming decomposition techniques widely used for traditional revenue management to develop an intuitive capacity control approach that allows for the incorporation of opaque products. In a simulation study, we show that the developed approach significantly outperforms other well-known capacity control approaches adapted to the opaque product setting. Based on the approach, we also provide computational examples of how the share of opaque products as well as the degree of opacity can influence the results.

ProFormA: An XML-based exchange format for programming tasks

Unterstützungssysteme für die Programmierausbildung sind weit verbreitet, doch gängige Standards für den Austausch von allgemeinen (Lern-) Inhalten und Tests erfüllen nicht die speziellen Anforderungen von Programmieraufgaben wie z. B. den Umgang mit komplexen Einreichungen aus mehreren Dateien oder die Kombination verschiedener (automatischer) Bewertungsverfahren. Dadurch können Aufgaben nicht zwischen Systemen ausgetauscht werden, was aufgrund des hohen Aufwands für die Entwicklung guter Aufgaben jedoch wünschenswert wäre. In diesem Beitrag wird ein erweiterbares XML-basiertes Format zum Austausch von Programmieraufgaben vorgestellt, das bereits von mehreren Systemen prototypisch genutzt wird. Die Spezifikation des Austauschformats ist online verfügbar [PFMA].

Ethnographic and epidemiologic approaches for the programming of prenatal and birth services in Mesa los Hornos, Mexico

The investigator conducted an action-oriented investigation of pregnancy and birth among the women of Mesa los Hornos, an urban squatter slum in Mexico City. Three aims guided the project: (1) To obtain information for improving prenatal and maternity service utilization; (2) To examine the utility of rapid ethnographic and epidemiologic assessment methodologies; (3) To cultivate community involvement in health development.^ Viewing service utilization as a culturally-bound decision, the study included a qualitative phase to explore women's cognition of pregnancy and birth, their perceived needs during pregnancy, and their criteria of service acceptability. A probability-based community survey delineated parameters of service utilization and pregnancy health events, and probed reasons for decisions to use medical services, lay midwives, or other sources of prenatal and labor and delivery assistance. Qualitative survey of service providers at relevant clinics, hospitals, and practices contributed information on service availability and access, and on coordination among private, social security, and public assistance health service sectors. The ethnographic approach to exploring the rationale for use or non-use of services provided a necessary complement to conventional barrier-based assessment, to inform planning of culturally appropriate interventions.^ Information collection and interpretation was conducted under the aegis of an advisory committee of community residents and service agency representatives; the residents' committee formulated recommendations for action based on findings, and forwarded the mandate to governmental social and urban development offices. Recommendations were designed to inform and develop community participation in health care decision-making.^ Rapid research methods are powerful tools for achieving community-based empowerment toward investigation and resolution of local health problems. But while ethnography works well in synergy with quantitative assessment approaches to strengthen the validity and richness of short-term field work, the author strongly urges caution in application of Rapid Ethnographic Assessments. An ethnographic sensibility is essential to the research enterprise for the development of an active and cooperative community base, the design and use of quantitative instruments, the appropriate use of qualitative techniques, and the interpretation of culturally-oriented information. However, prescribed and standardized Rapid Ethnographic Assessment techniques are counter-productive if used as research short-cuts before locale- and subject-specific cultural understanding is achieved. ^

Relational and Allegorical Semantics for Constraint Logic Programming

El cálculo de relaciones binarias fue creado por De Morgan en 1860 para ser posteriormente desarrollado en gran medida por Peirce y Schröder. Tarski, Givant, Freyd y Scedrov demostraron que las álgebras relacionales son capaces de formalizar la lógica de primer orden, la lógica de orden superior así como la teoría de conjuntos. A partir de los resultados matemáticos de Tarski y Freyd, esta tesis desarrolla semánticas denotacionales y operacionales para la programación lógica con restricciones usando el álgebra relacional como base. La idea principal es la utilización del concepto de semántica ejecutable, semánticas cuya característica principal es el que la ejecución es posible utilizando el razonamiento estándar del universo semántico, este caso, razonamiento ecuacional. En el caso de este trabajo, se muestra que las álgebras relacionales distributivas con un operador de punto fijo capturan toda la teoría y metateoría estándar de la programación lógica con restricciones incluyendo los árboles utilizados en la búsqueda de demostraciones. La mayor parte de técnicas de optimización de programas, evaluación parcial e interpretación abstracta pueden ser llevadas a cabo utilizando las semánticas aquí presentadas. La demostración de la corrección de la implementación resulta extremadamente sencilla. En la primera parte de la tesis, un programa lógico con restricciones es traducido a un conjunto de términos relacionales. La interpretación estándar en la teoría de conjuntos de dichas relaciones coincide con la semántica estándar para CLP. Las consultas contra el programa traducido son llevadas a cabo mediante la reescritura de relaciones. Para concluir la primera parte, se demuestra la corrección y equivalencia operacional de esta nueva semántica, así como se define un algoritmo de unificación mediante la reescritura de relaciones. La segunda parte de la tesis desarrolla una semántica para la programación lógica con restricciones usando la teoría de alegorías—versión categórica del álgebra de relaciones—de Freyd. Para ello, se definen dos nuevos conceptos de Categoría Regular de Lawvere y _-Alegoría, en las cuales es posible interpretar un programa lógico. La ventaja fundamental que el enfoque categórico aporta es la definición de una máquina categórica que mejora e sistema de reescritura presentado en la primera parte. Gracias al uso de relaciones tabulares, la máquina modela la ejecución eficiente sin salir de un marco estrictamente formal. Utilizando la reescritura de diagramas, se define un algoritmo para el cálculo de pullbacks en Categorías Regulares de Lawvere. Los dominios de las tabulaciones aportan información sobre la utilización de memoria y variable libres, mientras que el estado compartido queda capturado por los diagramas. La especificación de la máquina induce la derivación formal de un juego de instrucciones eficiente. El marco categórico aporta otras importantes ventajas, como la posibilidad de incorporar tipos de datos algebraicos, funciones y otras extensiones a Prolog, a la vez que se conserva el carácter 100% declarativo de nuestra semántica. ABSTRACT The calculus of binary relations was introduced by De Morgan in 1860, to be greatly developed by Peirce and Schröder, as well as many others in the twentieth century. Using different formulations of relational structures, Tarski, Givant, Freyd, and Scedrov have shown how relation algebras can provide a variable-free way of formalizing first order logic, higher order logic and set theory, among other formal systems. Building on those mathematical results, we develop denotational and operational semantics for Constraint Logic Programming using relation algebra. The idea of executable semantics plays a fundamental role in this work, both as a philosophical and technical foundation. We call a semantics executable when program execution can be carried out using the regular theory and tools that define the semantic universe. Throughout this work, the use of pure algebraic reasoning is the basis of denotational and operational results, eliminating all the classical non-equational meta-theory associated to traditional semantics for Logic Programming. All algebraic reasoning, including execution, is performed in an algebraic way, to the point we could state that the denotational semantics of a CLP program is directly executable. Techniques like optimization, partial evaluation and abstract interpretation find a natural place in our algebraic models. Other properties, like correctness of the implementation or program transformation are easy to check, as they are carried out using instances of the general equational theory. In the first part of the work, we translate Constraint Logic Programs to binary relations in a modified version of the distributive relation algebras used by Tarski. Execution is carried out by a rewriting system. We prove adequacy and operational equivalence of the semantics. In the second part of the work, the relation algebraic approach is improved by using allegory theory, a categorical version of the algebra of relations developed by Freyd and Scedrov. The use of allegories lifts the semantics to typed relations, which capture the number of logical variables used by a predicate or program state in a declarative way. A logic program is interpreted in a _-allegory, which is in turn generated from a new notion of Regular Lawvere Category. As in the untyped case, program translation coincides with program interpretation. Thus, we develop a categorical machine directly from the semantics. The machine is based on relation composition, with a pullback calculation algorithm at its core. The algorithm is defined with the help of a notion of diagram rewriting. In this operational interpretation, types represent information about memory allocation and the execution mechanism is more efficient, thanks to the faithful representation of shared state by categorical projections. We finish the work by illustrating how the categorical semantics allows the incorporation into Prolog of constructs typical of Functional Programming, like abstract data types, and strict and lazy functions.

Parallelizing irregular and pointer-based computations automatically: perspectives from logic and constraint programming

Irregular computations pose sorne of the most interesting and challenging problems in automatic parallelization. Irregularity appears in certain kinds of numerical problems and is pervasive in symbolic applications. Such computations often use dynamic data structures, which make heavy use of pointers. This complicates all the steps of a parallelizing compiler, from independence detection to task partitioning and placement. Starting in the mid 80s there has been significant progress in the development of parallelizing compilers for logic pro­gramming (and more recently, constraint programming) resulting in quite capable paralle­lizers. The typical applications of these paradigms frequently involve irregular computations, and make heavy use of dynamic data structures with pointers, since logical variables represent in practice a well-behaved form of pointers. This arguably makes the techniques used in these compilers potentially interesting. In this paper, we introduce in a tutoríal way, sorne of the problems faced by parallelizing compilers for logic and constraint programs and provide pointers to sorne of the significant progress made in the area. In particular, this work has resulted in a series of achievements in the areas of inter-procedural pointer aliasing analysis for independence detection, cost models and cost analysis, cactus-stack memory management, techniques for managing speculative and irregular computations through task granularity control and dynamic task allocation such as work-stealing schedulers), etc.

Designing a high performance parallel logic programming system

Compilation techniques such as those portrayed by the Warren Abstract Machine(WAM) have greatly improved the speed of execution of logic programs. The research presented herein is geared towards providing additional performance to logic programs through the use of parallelism, while preserving the conventional semantics of logic languages. Two áreas to which special attention is given are the preservation of sequential performance and storage efficiency, and the use of low overhead mechanisms for controlling parallel execution. Accordingly, the techniques used for supporting parallelism are efficient extensions of those which have brought high inferencing speeds to sequential implementations. At a lower level, special attention is also given to design and simulation detail and to the architectural implications of the execution model behavior. This paper offers an overview of the basic concepts and techniques used in the parallel design, simulation tools used, and some of the results obtained to date.

Some challenges for constraint programming

We propose a number of challenges for future constraint programming systems, including improvements in implementation technology (using global analysis based optimization and parallelism), debugging facilities, and the extensión of the application domain to distributed, global programming. We also briefly discuss how we are exploring techniques to meet these challenges in the context of the development of the CIAO constraint logic programming system.

A framework for assertion-based debugging in constraint logic programming

We propose a general framework for assertion-based debugging of constraint logic programs. Assertions are linguistic constructions which allow expressing properties of programs. We define assertion schemas which allow writing (partial) specifications for constraint logic programs using quite general properties, including user-defined programs. The framework is aimed at detecting deviations of the program behavior (symptoms) with respect to the given assertions, either at compile-time or run-time. We provide techniques for using information from global analysis both to detect at compile-time assertions which do not hold in at least one of the possible executions (i.e., static symptoms) and assertions which hold for all possible executions (i.e., statically proved assertions). We also provide program transformations which introduce tests in the program for checking at run-time those assertions whose status cannot be determined at compile-time. Both the static and the dynamic checking are provably safe in the sense that all errors flagged are definite violations of the specifications. Finally, we report on an implemented instance of the assertion language and framework.

IDRA (IDeal Resource Allocation): Computing ideal speedups in parallel logic programming

We present a technique to estimate accurate speedups for parallel logic programs with relative independence from characteristics of a given implementation or underlying parallel hardware. The proposed technique is based on gathering accurate data describing one execution at run-time, which is fed to a simulator. Alternative schedulings are then simulated and estimates computed for the corresponding speedups. A tool implementing the aforementioned techniques is presented, and its predictions are compared to the performance of real systems, showing good correlation.

Programming with global analysis

Global data-flow analysis of (constraint) logic programs, which is generally based on abstract interpretation [7], is reaching a comparatively high level of maturity. A natural question is whether it is time for its routine incorporation in standard compilers, something which, beyond a few experimental systems, has not happened to date. Such incorporation arguably makes good sense only if: • the range of applications of global analysis is large enough to justify the additional complication in the compiler, and • global analysis technology can deal with all the features of "practical" languages (e.g., the ISO-Prolog built-ins) and "scales up" for large programs. We present a tutorial overview of a number of concepts and techniques directly related to the issues above, with special emphasis on the first one. In particular, we concéntrate on novel uses of global analysis during program development and debugging, rather than on the more traditional application área of program optimization. The idea of using abstract interpretation for validation and diagnosis has been studied in the context of imperative programming [2] and also of logic programming. The latter work includes issues such as using approximations to reduce the burden posed on programmers by declarative debuggers [6, 3] and automatically generating and checking assertions [4, 5] (which includes the more traditional type checking of strongly typed languages, such as Gódel or Mercury [1, 8, 9]) We also review some solutions for scalability including modular analysis, incremental analysis, and widening. Finally, we discuss solutions for dealing with meta-predicates, side-effects, delay declarations, constraints, dynamic predicates, and other such features which may appear in practical languages. In the discussion we will draw both from the literature and from our experience and that of others in the development and use of the CIAO system analyzer. In order to emphasize the practical aspects of the solutions discussed, the presentation of several concepts will be illustrated by examples run on the CIAO system, which makes extensive use of global analysis and assertions.

Automatic parallelization of irregular and pointer-based computations: perspectives from logic and constraint programming

Irregular computations pose some of the most interesting and challenging problems in automatic parallelization. Irregularity appears in certain kinds of numerical problems and is pervasive in symbolic applications. Such computations often use dynamic data structures which make heavy use of pointers. This complicates all the steps of a parallelizing compiler, from independence detection to task partitioning and placement. In the past decade there has been significant progress in the development of parallelizing compilers for logic programming and, more recently, constraint programming. The typical applications of these paradigms frequently involve irregular computations, which arguably makes the techniques used in these compilers potentially interesting. In this paper we introduce in a tutorial way some of the problems faced by parallelizing compilers for logic and constraint programs. These include the need for inter-procedural pointer aliasing analysis for independence detection and having to manage speculative and irregular computations through task granularity control and dynamic task allocation. We also provide pointers to some of the progress made in these áreas. In the associated talk we demónstrate representatives of several generations of these parallelizing compilers.