PhD Theses on Association for Constraint Programming

A Unified Analytical Foundation for Constraint Handling Rules

Mon, 01 Jan 0001 00:00:00 +0000

Author: Hariolf Betz
School: University of Ulm
Graduated: August 21, 2014

Advanced Modelling and Search Techniques for Routing and Scheduling Problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Charles Thomas
School: UCLouvain
Supervisors: Pierre Schaus
Graduated: February 08, 2023
Link to full text: </theses/theseCharlesThomasFinal.pdf>

Analysis, Synthesis and Application of Automaton-Based Constraint Descriptions

Mon, 01 Jan 0001 00:00:00 +0000

Author: Maria Andreina Francisco Rodriguez
School: Uppsala University
Supervisors: Justin PearsonPierre Flener
Graduated: December 15, 2017
Link to full text: http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Auu%3Adiva-332149

Applications of Matching Theory in Constraint Programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Radosław Cymer
School: University of Hannover
Supervisors: Heribert Vollmer
Graduated: December 16, 2013

Asynchronously solving problems with privacy requirements

Mon, 01 Jan 0001 00:00:00 +0000

Author: Marius-Calin Silaghi
School: Swiss Federal Institute of Technology (EPFL)
Supervisors: Boi Faltings
Graduated: January 06, 2002
Link to full text: Asynchronously solving problems with privacy requirements

Abstract

Asynchronously solving problems with privacy requirements

Mon, 01 Jan 0001 00:00:00 +0000

Author: Marius-Calin Silaghi
School: Swiss Federal Institute of Technology (EPFL)
Supervisors: Boi Faltings
Graduated: August 02, 2002
Link to full text: Asynchronously solving problems with privacy requirements

Abstract

Asynchronous search techniques for distributed satisfaction problems are among the scientific challenges of Distributed Artificial Intelligence. They are useful in applications like negotiations (Generalized English Auctions), distributed meeting scheduling with private constraints, distributed configuration with secret know-how. Asynchronous search also has an important theoretic value, defining generic techniques whose properties, once proven, apply to a large number of not only distributed but also centralized old and new algorithms. A convincing example is the technique I propose for using backtracking nogoods in consistency (Section~\ref{sec:AMC:dmac-abt}), technique that can be straightforwardly applied to classic centralized versions of backtracking (as I show in Section~\ref{sec:DPF:FBT}) with improvements of efficiency. Despite the continuous effort of the last ten years, several important issues remained open until now. This thesis describes several advances that allow the agents to get more opportunities to communicate. It also proposes new frameworks for distributed constraint satisfaction, and strategies for agents. The main focus is on asynchronous complete search with polynomial space requirements in the size of the global problem, where the global problem is distributed naturally. Among the main contributions I mention: * Local consistency is a technique bringing gains that are exponential in the size of the problem, by deposing an effort that is exponential in the size of the used predicates (constraints). The effort can therefore be polynomial in the size of the global problem. {\bf Exponential gain with polynomial cost} made local consistency the most important technique in constraint satisfaction where it was shown that any opportunity must be used for exploiting it ({\em consistency maintenance}). I generalize the concept of centralized consistency maintenance in a way that makes it applicable to asynchronous techniques. This is the first technique allowing for consistency maintenance in asynchronous search and is very efficient (Chapter~\ref{chap:AMC}). * I noticed that the use of CSPs techniques for numeric problems over \R{} was possible by something that can be described as: {\em letting the domain splits have existential meaning rather than the universal meaning in classical backtracking}. Existing asynchronous search protocols supported only proposals with universal quantifiers (e.g. {\bf require}($\forall x\in{1})$). I modify and generalize existing asynchronous search protocols to support proposals with existential quantifiers (e.g. {\bf require}($\exists x\in{[1.5,10^5]\cup{0,1}}$)). This efficient technique is the first to allow a {\bf proper use of intervals} in proposals and is a first step I took for extending the applicability of asynchronous search to Numeric CSPs. Since this idea came when I tried to integrate in ABT an aggregation technique described in~\cite{rfia00}, the first protocol with the desired property is called Asynchronous Aggregation Search (see Chapter~\ref{chap:AAS}). * Existing algorithms enforced a static priority on agents. This is a drawback. It entangles agents to {\em verify conflicts in Generalized English Auctions} (Chapter~\ref{chap:ASS}), for defending themselves from unfair competition. One only knew to remove that constraint at the expense of requiring an unbounded computer memory (e.g. RAM). This is infeasible and leads to unwarranted protocols. I designed the first solving protocol that {\bf allows the needed dynamic reordering} and still has {\bf a guaranteed termination} with polynomially bounded computer space requirements. Moreover, the designed technique is very general as it can introduce all important reordering heuristics used for efficiency in centralized techniques (see Chapter~\ref{chap:ABTR}). * Besides the aforementioned reordering technique, I propose a suite of other techniques for dynamic reordering with other types of heuristics and still offering guaranteed termination with polynomial space requirements: Reordering with increasing delay restarts (Section~\ref{sec:ABTR:restarts}), and Finite number of reorderings. These two techniques are simple but allow only simplistic reordering heuristics. Dynamic Partial Order is discussed in Section~\ref{sec:ABTR:poas}. * Proposals with existential quantifiers are not sufficient for enabling application of search to numeric problems. The remaining requirement is that {\em proposals must not be binding}. Actually, one of the powerful and general available centralized techniques for solving numeric problems is the one proposed in Section~\ref{sec:cDPF:uca6}. Domains are split recursively, and {\bf the same constraints are recursively reverified} until complete satisfaction or an acceptable resolution is obtained. * Asynchronous techniques requested an agent only once its proposal. The proposals were therefore necessarily binding. This is incompatible with the practice required for numeric problems, as mentioned at the previous item. After understanding this problem, I proposed a modeling technique called Replica-based search. Several replicas of the same agent are asked proposals at different levels of abstractions. As a result, {\bf proposals of the replicas are not binding}. They can be refused by subsequent replicas (e.g. After an agent sends {\bf require}($\exists x\in{[1.5,10^5]\cup{0,1}}$), it can be more restrictive in a subsequent replica, {\bf require}($\exists x\in{[15,1000]}$), and later can even abandon completely its proposal, {\bf require}($x\not\in{[1.5,10^5]\cup{0,1}}$)). This technique, described in Chapter~\ref{chap:RDisCSP} closes the sequence of ideas required for developing asynchronous techniques for numeric problems over \R. * One of the main motivations for distributed algorithms is the enforcement of privacy. A framework for modeling and quantifying privacy loss and a {\bf theoretic comparison on the power of different protocols in saving privacy} are proposed in Chapter~\ref{chap:Privacy}. * Hewlett-Packard, learning that the ink-jet printer is a competitor for their main product, the laser printer, decided to also develop ink-jet printers in parallel. With the same principle in mind, I developed and analyzed an expensive secure protocol (SCSPS), proposed in Chapter~\ref{chap:Secure}. I also developed and analyzed {a set of cryptographic protocols for both discrete and numeric distributed satisfaction problems} (Annex~\ref{chap:Test}). For the work described in Annex~\ref{chap:Test}, another important contribution is the discovery of the feebleness of that type of techniques, the ``S-attacks’’. The obtained cryptographic protocols have some drawbacks such that a trade-off remains between the competing techniques. Secure protocols and distributed algorithms will continue to coexist for a while, as laser printers and ink-jet printers. * Several distributed optimization techniques have been developed so far. Those techniques are not general and abstract enough for allowing extensions like consistency maintenance and reordering. The notion of cost was unclear. I {\bf introduce the notion of cost in nogoods, obtaining a general stand-alone communication atom}. In Chapter~\ref{chap:Optimization} I describe a generalization of all the existing distributed satisfaction and optimization techniques. This generalization allows the introduction of consistency maintenance, reordering, etc. in distributed optimization. * Negotiations are defined by the fact that the participants yield from their constraints to reach common solutions. This is technically called: constraint relaxation. No relaxation technique existed for private problems. I propose such a technique for private constraints (Section~\ref{sec:GEA:CR}). A classical dualism for CSPs defines a standard transfer between privacy on constraints and privacy on domains (see Section~\ref{sec:domain-constraints} and Chapter~\ref{chap:DisGCSP}). We have also defined a primal version of this technique. SNEAKERS

Automatic Design of Feistel Ciphers Using Constraint Techniques

Mon, 01 Jan 0001 00:00:00 +0000

Author: Venkatesh Ramamoorthy
School: Florida Institute of Technology
Supervisors: Marius C. Silaghi
Graduated: July 01, 2010

Automatic Symmetry Detection and Dynamic Symmetry Breaking for Constraint Programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Christopher Mears
School: Monash University
Supervisors: Maria Garcia de la BandaMark Wallace
Graduated: October 01, 2010

Block-Coordinate Descent and Local Consistencies in Linear Programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Tomáš Dlask
School: Faculty of Electrical Engineering, Czech Technical University in Prague
Supervisors: Tomáš Werner
Graduated: November 24, 2022
Link to full text: https://dspace.cvut.cz/handle/10467/102874?locale-attribute=en

CDF-intervals: a probabilistic interval constraint framework to reason about data with uncertainty

Mon, 01 Jan 0001 00:00:00 +0000

Author: Aya Saad
School: Fakultät für Ingenieurwissenschaften, Informatik und Psychologie
Supervisors: Frühwirth, ThomGervet, Carmen
Graduated: April 21, 2016
Link to full text: https://oparu.uni-ulm.de/xmlui/handle/123456789/4005

CDF-Intervals: A Probabilistic Interval Constraint Framework to Reason about Data with Uncertainty

Mon, 01 Jan 0001 00:00:00 +0000

Author: Aya Saad
School: Fakultät für Ingenieurwissenschaften, Informatik und Psychologie
Supervisors: Gervet, CarmenFrühwirth, Thom
Graduated: April 21, 2016
Link to full text: https://oparu.uni-ulm.de/xmlui/handle/123456789/4005

Combining techniques of Bounded Model Checking and Constraint Programming to aid for error localization

Mon, 01 Jan 0001 00:00:00 +0000

Author: Mohammed Bekkouche
School: STIC doctoral School, Univ. Nice Sophia Antipolis
Supervisors: Michel RueherYahia LebbahHélène Collavizza
Graduated: December 11, 2015

Constrained Counting and Sampling: Bridging the Gap between Theory and Practice

Mon, 01 Jan 0001 00:00:00 +0000

Author: Kuldeep S. Meel
School: Rice University
Supervisors: Supratik Chakraborty, Moshe Y. Vardi
Graduated: December 31, 2017

Abstract

Constrained counting and sampling are two fundamental problems in Computer Science with numerous applications, including network reliability, privacy, probabilistic reasoning, and constrained-random verification. In constrained counting, the task is to compute the total weight, subject to a given weighting function, of the set of solutions of the given constraints. In constrained sampling, the task is to sample randomly, subject to a given weighting function, from the set of solutions to a set of given constraints. Consequently, constrained counting and sampling have been subject to intense theoretical and empirical investigations over the years. Prior work, however, offered either heuristic techniques with poor guarantees of accuracy or approaches with proven guarantees but poor performance in practice. In this thesis, we introduce a novel hashing-based algorithmic framework for constrained sampling and counting that combines the classical algorithmic technique of universal hashing with the dramatic progress made in combinatorial reasoning tools, in particular, SAT and SMT, over the past two decades. The resulting frameworks for counting ( ApproxMC2) and sampling (UniGen) can handle formulas with up to million variables representing a significant boost up from the prior state of the art tools’ capability to handle few hundreds of variables. If the initial set of constraints is expressed as Disjunctive Normal Form (DNF), ApproxMC2 is the only known Fully Polynomial Randomized Approximation Scheme (FPRAS) that does not involve Monte Carlo steps. By exploiting the connection between definability of formulas and variance of the distribution of solutions in a cell defined by 3-universal hash functions, we introduced an algorithmic technique, MIS, that reduced the size of XOR constraints employed in the underlying universal hash functions by as much as two orders of magnitude. We demonstrate the utility of the above techniques on real-world applications including probabilistic inference, design verification and estimating the reliability of critical infrastructure networks during natural disasters. The high parallelizability of our approach opens up new directions for development of artificial intelligence tools that can effectively leverage high-performance computing resources.

Constraint Programming Approaches to Electric Vehicle and Robot Routing Problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Kyle E. C. Booth
School: University of Toronto
Supervisors: J. Christopher Beck
Graduated: June 16, 2021
Link to full text: https://tspace.library.utoronto.ca/bitstream/1807/106297/2/Booth_Kyle_Ewart%20Coventry_202106_PhD_thesis.pdf

Constraint Programming for Optimization Under Uncertainty in Stochastic Inventory Control

Mon, 01 Jan 0001 00:00:00 +0000

Author: Roberto Rossi
School: Business School
Supervisors: S. Armagan Tarim, Brahim Hnich, Steven Prestwich
Graduated: December 11, 2008
Link to full text: Constraint Programming for Optimization Under Uncertainty in Stochastic Invento…

Abstract

Constraint Programming (CP) is a programming paradigm where relations between variables can be stated in the form of constraints. CP features discrete domains and global constraints. Global constraints capture interesting substructures of a problem, encapsulate dedicated inference algorithms based on feasibility and/or optimality reasoning, and provide information to the search process on the most viable course. Stochastic Constraint Programming (SCP) is a novel framework that generalizes CP stochastic problems, allowing both to model and solve this class of problems by using any available existing CP solver. Although this framework proves to be extremely flexible in terms of modelling power, its current implementation does not scale well. In order to enhance this framework, in this dissertation we propose a general extension for SCP: global chance-constraints. In contrast to global constraints, which represent relations among a non-fixed number of decision variables, global chance-constraints represent relations among a non-fixed number of decision variables and stochastic variables. Nevertheless, as global constraints do, global chance-constraints encapsulate dedicated inference algorithms based on feasibility and/or optimality reasoning and may provide information to the search process. We call optimization-oriented global chance-constraints those global chance-constraints performing optimality reasoning. We applied global chance-constraints encapsulating dedicated inference algorithms based on feasibility and/or optimality reasoning to problems in the area of stochastic inventory control. Our computational experience shows that global chance-constraints let us model and solve to optimality problems that could not or could be only approximately solved by other existing approaches. It also shows that filtering based on optimality reasoning is extremely effective for this class of problems.

Constraint Programming for Wireless Sensor Networks

Mon, 01 Jan 0001 00:00:00 +0000

Author: Farshid Hassani Bijarbooneh
School: Uppsala University
Supervisors: Justin PearsonPierre FlenerEdith Ngai
Graduated: March 13, 2015
Link to full text: http://uu.diva-portal.org/smash/get/diva2:778823/FULLTEXT01.pdf

Constraint Propagation – Models, Techniques, Implementation

Mon, 01 Jan 0001 00:00:00 +0000

Author: Guido Tack
School: Saarland University, Germany
Supervisors: Gert Smolka, Christian Schulte
Graduated: January 29, 2009

Abstract

This dissertation presents the design of a propagation-based constraint solver. The design is based on models that span several levels of abstraction, ranging from a mathematical foundation, to a high-level implementation architecture, to concrete data structures and algorithms. This principled design approach results in a well-understood, correct, modular, and efficient implementation.

The core of the developed architecture is the propagation kernel. It provides the propagation infrastructure and is thus crucial for correctness and efficiency of the solver. Based on a mathematical model as well as a careful design of the employed algorithms and data structures, the presented architecture results in an efficient and domain-independent kernel. Constraints are realized by propagators, and implementing a propagator is a challenging, error-prone, and time-consuming task. A practically useful solver must however provide a comprehensive propagator library. This dissertation introduces two techniques for automatically deriving correct and efficient propagators. Views generalize variables and are used to derive propagators from existing propagators. For constraints over set variables, propagators are derived from formal constraint specifications.

Constraint-Based Multi-Objective Combinatorial Optimization

Mon, 01 Jan 0001 00:00:00 +0000

Author: Miguel Terra-Neves
School: Instituto Superior Técnico, Universidade de Lisboa, Portugal
Supervisors: Vasco ManquinhoInês Lynce
Graduated: June 18, 2019

Constraint-Based Register Allocation and Instruction Scheduling

Mon, 01 Jan 0001 00:00:00 +0000

Author: Roberto Castañeda Lozano
School: KTH Royal Institute of Technology, Sweden
Supervisors: Christian Schulte, Mats Carlsson, Ingo Sander
Graduated: September 03, 2018
Link to full text: Open Access in DiVA database

Abstract

Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to improve latency or throughput) are central compiler problems. This dissertation proposes a combinatorial optimization approach to these problems that delivers optimal solutions according to a model, captures trade-offs between conflicting decisions, accommodates processor-specific features, and handles different optimization criteria.

Constraints for Membership in Formal Languages under Systematic Search and Stochastic Local Search

Mon, 01 Jan 0001 00:00:00 +0000

Author: Jun He
School: Uppsala University
Supervisors: Pierre FlenerJustin Pearson
Graduated: April 26, 2013
Link to full text: http://uu.diva-portal.org/smash/get/diva2:609984/FULLTEXT01.pdf

Constraints meet concurrency

Mon, 01 Jan 0001 00:00:00 +0000

Author: Jacopo Mauro
School: University of Bologna
Supervisors: Maurizio Gabbrielli
Graduated: May 10, 2012

Contributions au développement d’outils computationnels de design de protéines : méthodes et algorithmes de comptage avec garantie

Mon, 01 Jan 0001 00:00:00 +0000

Author: Clément Viricel
School: Université Paul Sabatier (for PhD)
Supervisors: Thomas Schiex, INRA, Toulouse, FranceSophie Barbe, INSA/LISBP, Toulouse, France
Graduated: December 18, 2017

Cost-based filtering algorithms for a capacitated lot sizing problem and the constrained arborescence problem

Mon, 01 Jan 0001 00:00:00 +0000

Author: HOUNDJI Vinasetan Ratheil
School: ICTEAM (UCL) - IFRI (UAC)
Supervisors: Laurence A. WolseyPierre SchausNorbert Hounkonnou
Graduated: May 31, 2017

Cost-based filtering algorithms for a Capacitated Lot Sizing Problem and the Constrained Arborescence Problem

Mon, 01 Jan 0001 00:00:00 +0000

Author: Houndji Vinasetan Ratheil
School: Université catholique de Louvain (UCL, Belgium) and Université d’Abomey-Calavi (UAC, Benin)
Supervisors: Laurence A. WolseyPierre SchausMahouton Norbert Hounkonnou
Graduated: May 31, 2017
Link to full text: https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A186333

Decision Diagrams for Optimization

Mon, 01 Jan 0001 00:00:00 +0000

Author: Andre Augusto Cire
School: Carnegie Mellon University
Supervisors: John Hooker, Willem-Jan van Hoeve
Graduated: August 08, 2014

Abstract

Decision diagrams are compact graphical representations of Boolean functions originally introduced for applications in circuit design, simulation, and formal verification. Recently, they have been considered for a variety of purposes in optimization and operations research. These include facet enumeration in integer programming, maximum flow computation in large-scale networks, solution counting in combinatorics, and learning in genetic programming techniques.

Decision Support for Disaster Management Through Hybrid Optimization

Mon, 01 Jan 0001 00:00:00 +0000

Author: Carleton Coffrin
School: Brown University
Supervisors: Pascal Van HentenryckRussell Bent
Graduated: May 27, 2012
Link to full text: http://cs.brown.edu/research/pubs/theses/phd/2012/coffrin.pdf

Decomposition-based Matheuristics for Green Vehicle Routing Problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Alejandro Fernández Gil
School: Departamento de Informática
Supervisors: Carlos CastroEduardo Lalla-Ruiz
Graduated: December 02, 2022
Link to full text: </theses/alejandro_fernandez_gil_-_decomposition-based_matheuristics_for_green_vehicle_routing_problems.pdf>

Effective Profiling of Tree Search in Constraint Programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Maxim Shishmarev
School: Faculty of Informaction Technology, Monash University, Australia
Supervisors: Guido TackMaria Garcia de la BandaChristopher Mears
Graduated: April 16, 2018

Efficient Algorithms for Strong Local Consistencies and Adaptive Techniques in Constraint Satisfaction Problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Anastasia Paparrizou
School: University of Western Macedonia, Greece
Supervisors: Kostas Stergiou
Graduated: December 10, 2013

Abstract

Constraint Programming (CP) is a successful technology for solving a wide range of problems in business and industry which require the satisfaction of a set of complex constraints. Examples include product configuration, resource allocation, transportation, and scheduling. As the simultaneous satisfaction of different constraints is intractable in general, problems can become very difficult to solve as their size increases. CP has thus developed various techniques to tackle this inherent problem. Enforcing a local consistency property is one of the most important such techniques. Bounds Consistency and Arc Consistency are the two most widely studied and used local consistencies in CP solvers. While there exist stronger local consistency (SLC) properties, their usage is limited due to their prohibitive cost.

Efficient algorithms to solve scheduling problems with a variety of optimization criteria

Mon, 01 Jan 0001 00:00:00 +0000

Author: HAMED FAHIMI
School: Université Laval
Graduated: September 30, 2016
Link to full text: http://theses.ulaval.ca/archimede/meta/32946

Efficient Propagators for Global Constraints

Mon, 01 Jan 0001 00:00:00 +0000

Author: Claude-Guy Quimper
School: University of Waterloo
Supervisors: Alejandro López-Ortiz
Graduated: October 21, 2006

Abstract

We study in this thesis three well known global constraints. The All-Different constraint restricts a set of variables to be assigned to distinct values. The global cardinality constraint (GCC) ensures that a value v is assigned to at least l_v variables and to at most u_v variables among a set of given variables where l_v and u_v are non-negative integers such that l_v <= u_v. The Inter-Distance constraint ensures that all variables, among a set of variables x_1,…, x_n, are pairwise distant from p, i.e. |x_i - x_j| >= p for all i != j. The All-Different constraint, the GCC, and the Inter-Distance constraint are largely used in scheduling problems. For instance, in scheduling problems where tasks with unit processing time compete for a single resource, we have an All-Different constraint on the starting time variables. When there are k resources, we have a GCC with l_v = 0 and u_v = k over all starting time variables. Finally, if tasks have processing time t and compete for a single resource, we have an Inter-Distance constraint with p = t over all starting time variables. We present new propagators for the \alldiff\ constraint, the \gcc, and the
\interdistance\ constraint i.e., new filtering algorithms that reduce the search space according to these constraints. For a given consistency, our propagators outperform previous propagators both in practice and in theory. The gains in performance are achieved through judicious use of advanced data structures combined with novel results on the structural properties of the constraints.

Efficient Search Procedures for Solving Combinatorial Problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Serdar Kadioglu
School: Brown University
Supervisors: Meinolf Sellmann
Graduated: May 27, 2012
Link to full text: http://cs.brown.edu/research/pubs/theses/phd/2012/kadioglu.pdf

Extensible automated constraint modelling via refinement of abstract problem specifications

Mon, 01 Jan 0001 00:00:00 +0000

Author: Özgür Akgün
School: School of Computer Science
Supervisors: Ian MiguelChris Jefferson
Graduated: May 01, 2014
Link to full text: </theses/OzgurAkgunPhdThesis.pdf>

Extensible Automated Constraint Modelling via Refinement of Abstract Problem Specifications

Mon, 01 Jan 0001 00:00:00 +0000

Author: Özgür Akgün
School: School of Computer Science, University of St Andrews
Supervisors: Ian MiguelChris Jefferson
Graduated: June 25, 2014
Link to full text: https://research-repository.st-andrews.ac.uk/handle/10023/6547

Floating-point numbers round-off error analysis by constraint programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Rémy Garcia
School: Université Côte d’Azur, Nice, France
Supervisors: Claude Michel, Université Côte d’Azur, Nice, FranceMichel Rueher, Université Côte d’Azur, Nice, France
Graduated: September 08, 2021
Link to full text: https://tel.archives-ouvertes.fr/tel-03416121

Formalizing Methods for Propositional Model Counting and Enumeration

Mon, 01 Jan 0001 00:00:00 +0000

Author: Sibylle Möhle
School: Johannes Kepler University Linz, Altenberger Strasse 69, 4040 Linz, Austria
Supervisors: Prof. Dr. Armin Biere, University of Freiburg, Freiburg im Breisgau, Germany
Graduated: July 01, 2022
Link to full text: </theses/sibylle_rosa_natalina_mohle-rotondi_-_formalizing_methods_for_propositional_model_counting_and_enumeration.pdf>

From Declarative Models to Local Search

Mon, 01 Jan 0001 00:00:00 +0000

Author: Gustav Björdal
School: Uppsala University
Supervisors: Pierre FlenerJustin Pearson
Graduated: April 23, 2021
Link to full text: http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Auu%3Adiva-436139

Generating Optimized and Secure Binary Code

Mon, 01 Jan 0001 00:00:00 +0000

Author: Rodothea Myrsini Tsoupidi
School: School of Electrical Engineering and Computer Science
Supervisors: Elena TroubitsynaPanagiotis Papadimitratos
Graduated: June 07, 2023
Link to full text: </theses/rodothea_myrsini_tsoupidi_-_generating_optimized_and_secure_binary_code.pdf>

Harnessing Tractability in Constraint Satisfaction Problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Clément Carbonnel
School: INP Toulouse
Supervisors: Emmanuel Hebrard, Martin C. Cooper
Graduated: December 07, 2016

Abstract

The generality of CSP and its connections to logic, universal algebra and graph theory have drawn considerable attention from the complexity theory community. In particular, a rich theory has been developed to study how restricting the catalog of available relations affects the complexity of CSP. This line of research has produced a series of increasingly sophisticated polynomial-time algorithms that solve CSPs over a variety of constraint languages; unfortunately, this wealth of results on tractable subproblems of CSP has seen little to no practical use so far. This dissertation discusses the technical obstacles that prevent these theoretical results from being relevant to practical solving, and hints at possible approaches to overcome them.

High-Level Modelling and Solving for Online, Real-Time, and Multiagent Combinatorial Optimisation

Mon, 01 Jan 0001 00:00:00 +0000

Author: Alexander Ek
School: Monash University
Supervisors: Guido TackMaria Garcia de la BandaAndreas SchuttPeter J. Stuckey
Graduated: September 23, 2022
Link to full text: </theses/Thesis%20Alexander%20Ek_1122.pdf>

Hybrid Methods for Resource Allocation and Scheduling Problems in Deterministic and Stochastic Environments

Mon, 01 Jan 0001 00:00:00 +0000

Author: Michele Lombardi
School: University of Bologna
Supervisors: Michela Milano
Graduated: April 01, 2010

Abstract

This work presents exact, hybrid algorithms for mixed resource Allocation and Scheduling problems; in general terms, those consist into assigning over time finite capacity resources to a set of precedence connected activities.

The proposed methods have broad applicability, but are mainly motivated by applications in the field of Embedded System Design. In particular, high-performance embedded computing recently witnessed the shift from single CPU platforms with application-specific accelerators to programmable Multi Processor Systems-on-Chip (MPSoCs). Those allow higher flexibility, real time performance and low energy consumption, but the programmer must be able to effectively exploit the platform parallelism. This raises interest in the development of algorithmic techniques to be embedded in CAD tools; in particular, given a specific application and platform, the objective if to perform optimal allocation of hardware resources and to compute an execution schedule.

Hybrid Optimization of Vehicle Routing Problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Edward Lam
School: University of Melbourne
Supervisors: Pascal Van Hentenryck
Graduated: April 17, 2019
Link to full text: Hybrid Optimization of Vehicle Routing Problems

Abstract

Vehicle routing problems are combinatorial optimization problems that aspire to design vehicle routes that minimize some measure of cost, such as the total distance traveled or the time at which the last vehicle returns to a depot, while adhering to various restrictions. Vehicle routing problems are of profound interest in both academia and industry because they are opportunities to study graph structures and algorithms, and because they underpin practical applications in a multitude of industries, but notably, the transportation and logistics industries. This dissertation presents two applications relevant to industry and develops a fully hybrid method for solving a classical vehicle routing problem.

Improving combinatorial optimization

Mon, 01 Jan 0001 00:00:00 +0000

Author: Geoffrey G Chu
School: The University of Melbourne, Australia
Supervisors: Peter J Stuckey
Graduated: October 01, 2011
Link to full text: Melbourne University Institutional Repository

Abstract

Combinatorial Optimization is an important area of computer science that has many theoretical and practical applications. In this thesis, we present important contributions to several different areas of combinatorial optimization, including nogood learning, symmetry breaking, dominance, relaxations and parallelization.

We develop a new nogood learning technique based on constraint projection that allows us to exploit subproblem dominances that arise when two different search paths lead to subproblems which are identical on the remaining unlabeled variables. On appropriate problems, this nogood learning technique provides orders of magnitude speedup compared to a base solver which does not learn nogoods.

Improving Scheduling by Learning

Mon, 01 Jan 0001 00:00:00 +0000

Author: Andreas Schutt
School: The University of Melbourne
Supervisors: Peter J. Stuckey
Graduated: June 30, 2011
Link to full text: http://hdl.handle.net/11343/36701

Instance-Specific Algorithm Configuration

Mon, 01 Jan 0001 00:00:00 +0000

Author: Yuri Malitsky
School: Brown University
Supervisors: Meinolf Sellmann
Graduated: May 16, 2012

Abstract

When developing a new heuristic or complete algorithm, we frequently face the problem of choice. There may be multiple heuristics that we can employ, different types of inference mechanisms, various settings for the learning rate, or a multitude of neighborhoods to choose from. Furthermore, the way in which the choices we make affect one another is not readily known. The task of making these choices is referred to as algorithm configuration. At the same time, even for instances from the same problem domain, very different algorithmic strategies may prove effective. The task of selecting the most appropriate solver for the provided problem instance is know as algorithm selection. While it is possible to approach both these tasks manually, the potential explosion of the search space necessitates that need for automated methodologies to provide consistently beneficial results. This thesis addresses this exact problem, showing how to effectively perform both algorithm selection and configuration simultaneously, and leading to approaches that have dominated international competitions.

Learning SAT Encodings for Constraint Satisfaction Problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Felix Ulrich-Oltean
School: University of York, United Kingdom
Supervisors: Peter Nightingale, James Walker, James Cussens
Graduated: March 22, 2024
Link to full text: </theses/fvuo-dissertation.pdf>

Making the Most of Structure in Constraint Models

Mon, 01 Jan 0001 00:00:00 +0000

Author: Kevin Leo
School: Monash University
Supervisors: Guido TackMaria Garcia de la Banda
Graduated: July 26, 2017
Link to full text: https://figshare.com/articles/Making_the_Most_of_Structure_in_Constraint_Models/5216329

New solving methods for constrained optimization problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Amine Lamine
School: National Engineering School of Sfax
Supervisors: Prof. Dr. Brahim HnichProf. Dr. Habib ChabchoubDr. Mahdi Khemakhem
Graduated: November 01, 2016

New Techniques for Discrete Optimization

Mon, 01 Jan 0001 00:00:00 +0000

Author: David Bergman
School: Tepper School of Business, Carnegie Mellon University
Supervisors: J.N. Hooker, Willem-Jan van Hoeve
Graduated: May 19, 2015
Link to full text: New Techniques for Discrete Optimization

Abstract

Objective function bounds are critical to solving discrete optimization problems. In this thesis, we explore several new techniques for obtaining bounds, and also describe a new branch-and-bound algorithm for discrete optimization problems.

The first area explored is the application of decision diagrams (DDs) to binary optimization problems. In recent years, DDs have been applied to various problems in operations research. These include sequential pattern data mining, cut generation, product configuration, and post-optimality analysis in integer programming, to name a few. Through these applications and others, DDs have proven to be a useful tool for a variety of tasks. Unfortunately, DDs may grow exponentially large, prohibiting their use.

On Algorithm Selection, with an Application to Combinatorial Search Problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Lars Kotthoff
School: University of St Andrews
Supervisors: Ian MiguelIan Gent
Graduated: June 20, 2012

On the foundations of constraint logic programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Peter J. Stuckey
School: Department of Data Science and Artificial Intelligence
Supervisors: Joxan Jaffar
Graduated: December 24, 1987
Link to full text: https://doi.org/10.26180/14972436.v1

Abstract

Stuckey., Peter J. (2020). On the foundations of constraint logic programming. Monash University. Thesis. https://doi.org/10.26180/14972436.v1

On the Hybridization of Constraint Programming and Local Search Techniques: Models and Software Tools

Mon, 01 Jan 0001 00:00:00 +0000

Author: Raffaele Cipriano
School: Univ. of Udine, DIMI
Supervisors: AGOSTINO DOVIERLUCA DI GASPERO
Graduated: April 01, 2011

On the use of graphs within constraint-programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Jean-Guillaume Fages
School: Ecole des Mines de Nantes, LINA, CNRS, INRIA
Supervisors: Xavier Lorca, Nicolas Beldiceanu
Graduated: October 23, 2014

Abstract

Many IT applications require to solve decision problems which are hard from a mathematical point of view. Constraint-programming enables to model and solve some of these problems. Among them, some are defined over graphs. Beyond the difficulty stemming from each of these problems, the size of the instance to solve increases the difficulty of the task. This PhD thesis is about the use of graphs within constraint- programming, in order to improve its scalability. First, we study the use of constraint-programming to solve some graph problems involving the computation of trees and Hamiltonian paths and cycles. These problems are important and can be found in many industrial applications. Both filtering and search are investigated. Next, we move on problems which are no longer defined in terms of graph properties. We then study the use of graphs to propagate global constraints. In particular, we suggest a generic schema, relying on a graph structure, to dynamically decompose filtering algorithms. The central theme in this work is the use of graph concepts at the origin of every reasoning and the practical will to increase the size of problems that can be addressed in constraint-programming.

On the use of tasks ordering to solve scheduling problems with constraint programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Arthur Godet
School: IMT Atlantique
Graduated: September 23, 2021
Link to full text: https://theses.hal.science/tel-03681868

Optimization Methods Based on Decision Diagrams for Constraint Programming, AI Planning, and Mathematical Programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Margarita Castro
School: University of Toronto
Supervisors: J. Christopher Beck, Andre A. Cire
Graduated: March 01, 2021
Link to full text: Optimization Methods Based on Decision Diagrams for Constraint Programming, AI …

Abstract

Decision diagrams (DDs) are graphical structures that can be used to solve discrete optimization problems by representing the set of feasible solutions as paths in a graph. This graphical encoding of the feasibility set can represent complex combinatorial structures and is the foundation of several novel optimization techniques. Due to their flexibility, DDs have become an attractive optimization tool for researchers in different fields, including operations research and computer science.

Other Things Besides Number: Abstraction, Constraint Propagation, and String Variable Types

Mon, 01 Jan 0001 00:00:00 +0000

Author: Joseph Scott
School: Uppsala University
Supervisors: Pierre FlenerJustin PearsonParosh Abdulla
Graduated: March 14, 2016
Link to full text: http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Auu%3Adiva-273311

Parallel Search for Maximum Satisfiability

Mon, 01 Jan 0001 00:00:00 +0000

Author: Ruben Martins
School: Instituto Superior Técnico, Universidade de Lisboa, Portugal
Supervisors: Inês LynceVasco Manquinho
Graduated: September 09, 2013
Link to full text: http://sat.inesc-id.pt/~ruben/papers/martins-phd.pdf

Peel and Bound: Solving Discrete Optimization Problems with Decision Diagrams and Separation

Mon, 01 Jan 0001 00:00:00 +0000

Author: Isaac Rudich
School: Polytechnique Montréal
Supervisors: Louis-Martin RousseauQuentin Cappart
Graduated: August 16, 2024
Link to full text: </theses/Thesis.pdf>

Portfolio Approaches in Constraint Programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Roberto Amadini
School: Department of Computer Science and Engineering - University of Bologna
Supervisors: Prof. Maurizio Gabbrielli
Graduated: June 04, 2015
Link to full text: http://www.cs.unibo.it/~amadini/thesis.pdf

Practical Tractability of CSPs by Higher Level Consistency and Tree Decomposition

Mon, 01 Jan 0001 00:00:00 +0000

Author: Shant Karakashian
School: University of Nebraska-Lincoln
Supervisors: Berthe Y Choueiry
Graduated: May 01, 2013
Link to full text: http://consystlab.unl.edu/our_work/Dissertations/Karakashian-Dissertation.pdf

Probabilistic Constraint Reasoning

Mon, 01 Jan 0001 00:00:00 +0000

Author: Elsa Carvalho
School: Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa
Supervisors: Jorge CruzPedro Barahona
Graduated: November 26, 2012

Propagation techniques of resource constraint for cumulative scheduling

Mon, 01 Jan 0001 00:00:00 +0000

Author: Roger Kameugne
School: University of Yaounde 1, Faculty of Sciences, Department of Mathematics.
Supervisors: Prof. Laure Pauline Fotso
Graduated: September 30, 2014

Reformulation of Global Constraints

Mon, 01 Jan 0001 00:00:00 +0000

Author: Nina Narodytska
School: UNSW
Supervisors: Toby WalshGeorge Katsirelos
Graduated: December 14, 2011

Reliable Constraint Reasoning with Uncertain Data

Mon, 01 Jan 0001 00:00:00 +0000

Author: Neil Yorke-Smith
School: Imperial College London
Supervisors: Carmen Gervet
Graduated: June 01, 2004

Abstract

Constraint programming (CP) has proved an effective paradigm to model and solve difficult combinatorial optimisation problems from disparate domains. The need for reliable solutions to real-world applications requires that the uncertainty inherent in these problems is taken into account. To date, however, there has been little work on accommodating uncertainty within CP, apart from seeking a solution that holds in the greatest number of circumstances.

Representations in Constraint Programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Christopher Jefferson
School: University of York
Graduated: June 30, 2007

Abstract

One important choice in modelling is what variables will be used to represent the states of the problem. This choice is often optimised to try to take advantage of the global constraints in the solver being used. This concentration on global constraints has allowed the performance of models to improve as more and better global constraints are designed and implemented. However, this obsession with global constraints has lead to a lack of study of the variables, independent of the particular set of constraints implemented by a given solver.

Resource-constrained multi-project scheduling problem: taxonomy, variants, and approaches

Mon, 01 Jan 0001 00:00:00 +0000

Author: Mariam Gómez Sánchez
School: Departamento de Informática
Supervisors: Carlos CastroEduardo Lalla-Ruiz
Graduated: December 14, 2023
Link to full text: </theses/mariam_gomez_sanchez_-_resource-constrained_multi-project_scheduling_problem_taxonomy_variants_and_approaches.pdf>

Robust Solutions for Constraint Satisfaction and Optimisation under Uncertainty

Mon, 01 Jan 0001 00:00:00 +0000

Author: Emmanuel Hebrard
School: University of New South Wales
Supervisors: Toby Walsh, Brahim Hnich, Michael Maher
Graduated: April 20, 2007
Link to full text: Robust Solutions for Constraint Satisfaction and Optimisation under Uncertainty

Abstract

We develop a framework for finding robust solutions of constraint programs. Our approach is based on the notion of fault tolerance. We formalise this concept within constraint programming, extend it in several dimensions and introduce some algorithms to find robust solutions efficiently.

Robustness and stability in dynamic constraint satisfaction problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Laura Climent
School: Universitat Poitècnica de València
Supervisors: Miguel A. SalidoFederico Barber
Graduated: December 17, 2013
Link to full text: http://hdl.handle.net/10251/34785

Scaling DCOP algorithms for cooperative multi-agent coordination

Mon, 01 Jan 0001 00:00:00 +0000

Author: Marc Pujol-Gonzalez
School: Universitat Autonoma de Barcelona (UAB)
Supervisors: Jesus CerquidesPedro MeseguerJuan A. Rodriguez-Aguilar
Graduated: November 25, 2014
Link to full text: http://www.iiia.csic.es/files/pdfs/2014pujolscali.pdf

Scheduling Through Logic-Based Tools

Mon, 01 Jan 0001 00:00:00 +0000

Author: Jordi Coll Caballero
School: Universitat de Girona
Supervisors: Josep Suy FranchMateu Villaret Auselle
Graduated: July 15, 2019
Link to full text: https://dugi-doc.udg.edu/handle/10256/17246

Search and Coverage Path Planning

Mon, 01 Jan 0001 00:00:00 +0000

Author: Michael Morin
School: Department of Computer Science and Software Engineering, Université Laval, Québec, QC, Canada
Graduated: December 31, 2015
Link to full text: http://www.theses.ulaval.ca/2015/32066/

Search, propagation, and learning in sequencing and scheduling problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Mohamed Siala
School: Université Fédérale Toulouse Midi-Pyrénées
Supervisors: Emmanuel HebrardChristian Artigues
Graduated: May 13, 2015

Set Constraints for Local Search

Mon, 01 Jan 0001 00:00:00 +0000

Author: Magnus Ågren (now Magnus Rattfeldt)
School: Uppsala University
Supervisors: Pierre Flener, Justin Pearson
Graduated: January 18, 2008
Link to full text: Set Constraints for Local Search

Abstract

Combinatorial problems are ubiquitous in our society and solving such problems efficiently is often crucial. One technique for solving combinatorial problems is constraint-based local search. Its compositional nature together with its efficiency on large problem instances have made this technique particularly attractive. In this thesis we contribute to simplifying the solving of combinatorial problems using constraint-based local search.

Shop and batch scheduling with constraints

Mon, 01 Jan 0001 00:00:00 +0000

Author: Arnaud Malapert
School: Université de Nantes
Supervisors: Narendra JussienLouis-Martin RousseauChristelle GuéretAndré Langevin
Graduated: September 09, 2011
Link to full text: https://tel.archives-ouvertes.fr/tel-00630122

Soft Constraints in MiniBrass: Foundations and Applications

Mon, 01 Jan 0001 00:00:00 +0000

Author: Alexander Schiendorfer
School: University of Augsburg
Supervisors: Wolfgang Reif
Graduated: November 12, 2018

Solving Balancing and Bin-Packing problems with Constraint Programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Pierre Schaus
School: UCLouvain
Supervisors: Yves DevillesPierre DupontJean-Charles Régin
Graduated: August 31, 2009
Link to full text: https://www.a4cp.org/index.php/images/default_thumbnail.jpg

Solving Hard Subgraph Problems in Parallel

Mon, 01 Jan 0001 00:00:00 +0000

Author: Ciaran McCreesh
School: School of Computing Science
Supervisors: Patrick Prosser
Graduated: July 25, 2017
Link to full text: Solving hard subgraph problems in parallel

Abstract

This thesis improves the state of the art in exact, practical algorithms for finding subgraphs. We study maximum clique, subgraph isomorphism, and maximum common subgraph problems. These are widely applicable: within computing science, subgraph problems arise in document clustering, computer vision, the design of communication protocols, model checking, compiler code generation, malware detection, cryptography, and robotics; beyond, applications occur in biochemistry, electrical engineering, mathematics, law enforcement, fraud detection, fault diagnosis, manufacturing, and sociology. We therefore consider both the ``pure’’ forms of these problems, and variants with labels and other domain-specific constraints.

Solving Optimization Problems via Maximum Satisfiability : Encodings and Re-Encodings

Mon, 01 Jan 0001 00:00:00 +0000

Author: Jeremias Berg
School: University of Helsinki
Supervisors: Associate Professor Matti Järvisalo, Professor Petri Myllymäki
Graduated: June 13, 2018
Link to full text: University of Helsinki e-thesis

Abstract

NP-hard combinatorial optimization problems are commonly encountered in numerous different domains. As such efficient methods for solving instances of such problems can save time, money, and other resources in several different applications. This thesis investigates exact declarative approaches to combinatorial optimization within the maximum satisfiability (MaxSAT) paradigm, using propositional logic as the constraint language of choice. Specifically we contribute to both MaxSAT solving and encoding techniques.

Solving Scheduling Problems under Disruptions

Mon, 01 Jan 0001 00:00:00 +0000

Author: Alexandre Lemos
School: UNIVERSIDADE DE LISBOA, INSTITUTO SUPERIOR TÉCNICO
Supervisors: Inês LyncePedro T Monteiro
Graduated: July 08, 2021

Spacetime Programming: A Synchronous Language for Constraint Search

Mon, 01 Jan 0001 00:00:00 +0000

Author: Pierre Talbot
School: Sorbonne University
Supervisors: Carlos AgonPhilippe Esling
Graduated: June 06, 2019
Link to full text: http://hyc.io/thesis.html

Strong Consistencies for Weighted Constraint Satisfaction Problems

Mon, 01 Jan 0001 00:00:00 +0000

Author: Thi Hong Hiep NGUYEN
School: Université de Toulouse 3 Paul Sabatier
Supervisors: Thomas SchiexChristian Bessiere
Graduated: January 15, 2015
Link to full text: </theses/rolandgarros26.pdf>

The complexity and expressive power of valued constraints

Mon, 01 Jan 0001 00:00:00 +0000

Author: Stanislav Zivny
School: University of Oxford
Supervisors: Peter Jeavons
Graduated: March 06, 2010
Link to full text: The complexity and expressive power of valued constraints

Abstract

This thesis is a detailed examination of the expressive power of valued constraints and related complexity questions. The valued constraint satisfaction problem (VCSP) is a generalisation of the constraint satisfaction problem which allows a variety of combinatorial optimisation problems to be described. Although most results are stated in this framework, they can be interpreted equivalently in the framework of, for instance, pseudo-Boolean polynomials, Gibbs energy minimisation or Markov Random Fields.
We take a result of Cohen, Cooper and Jeavons that characterises the expressive power of valued constraints in terms of certain algebraic properties, and extend this result by showing a new connection between the expressive power of valued constraints and linear programming. We prove a decidability result for related algebraic properties.

The extensional constraint

Mon, 01 Jan 0001 00:00:00 +0000

Author: Hélène Verhaeghe
School: UCLouvain, Louvain-la-Neuve, Belgium
Supervisors: Prof. Pierre Schaus, Prof. Christophe Lecoutre
Graduated: August 31, 2021

Abstract

Extensional constraints are crucial in Constraint Programming. They represent allowed combinations of values for a subset of variables (the scope of the constraint) using extensional representation forms such as tables (lists of tuples of constraint solutions) or MDDs (layered acyclic directed graphs where each path represents a constraint solution). Such extensional forms allow the modelization of virtually any kind of constraints. This type of constraint is among the first ones available in constraint solvers. A lot of progress has been made since the original design of the first propagator of table constraints: advanced use of supports, simple tabular reduction, bitwise computations, reseting opperations, table compression, and MDDs. The most recent algorithm prior to this thesis is Compact-Table. It advantageously uses a data structure called reversible sparse bitsets to speed up the computations. The work in this thesis initiates with Compact-Table. The goal is to extend it to handle other kinds of extensional representation. The first addressed representation is about compressed tables, i.e. tables containing tuples wich do not only contain single values but also simple unary (∗, != v, ≤ v, ≥ v, ∈ S, !∈ S) or binary (= x + v, != x + v, ≤ x + v, ≥ x + v) restrictions. One such compressed tuple allows representing several classical ones. This led to the CT ∗ and CT bs algorithms, handling respectively short and basic smart tables. The second addressed issue concerns negative tables, i.e. tables listing forbidden combinations of values. This results in the CT neg and CT ∗ neg algorithms, handling respectively negative and negative short tables. The third and last adaptation addresses diagram structures, i.e. graphs such as MDDs or other layered graphical structure. This led to the CD and CD bs algorithms, handling respectively diagrams and basic smart diagrams. Being able to use such diversity of representation helps to counter-balance the main drawback of classical table representations, which is their potentially exponential growth in size. Compressed tables, negative tables, and diagrams help reduce the memory consumption (storage size) required to store an equivalent representation.

Towards Next Generation Sequential and Parallel SAT Solvers

Mon, 01 Jan 0001 00:00:00 +0000

Author: Norbert Manthey
School: TU Dresden
Supervisors: Prof. Steffen Hölldober
Graduated: December 01, 2014
Link to full text: http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-158672

Towards Statistical Consistency for Stochastic Constraint Programming

Mon, 01 Jan 0001 00:00:00 +0000

Author: Imen Zghidi
School: FSEGS
Supervisors: Prof. Dr. Abdelwahab Rebaii
Graduated: May 14, 2016

Transformations of representation in constraint satisfaction

Mon, 01 Jan 0001 00:00:00 +0000

Author: András Z. Salamon
School: University of Oxford
Supervisors: Peter Jeavons
Graduated: January 24, 2015
Link to full text: http://ora.ox.ac.uk/objects/uuid:5d641fff-4d95-43b2-9ff8-73395d782ad8

Universal Instruction Selection

Mon, 01 Jan 0001 00:00:00 +0000

Author: Gabriel Hjort Blindell (now Gabriel Hjort Åkerlund)
School: KTH Royal Institute of Technology
Supervisors: Christian SchulteMats CarlssonIngo Sander
Graduated: April 06, 2018