Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases

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    647 research outputs found

    An Exact Algorithm for the Steiner Forest Problem

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    The Steiner forest problem asks for a minimum weight forest that spans a given number of terminal sets. The problem has famous linear programming based 2-approximations [Agrawal et al., 1995; Goemans and Williamson, 1995; Jain, 2001] whose bottleneck is the fact that the most natural formulation of the problem as an integer linear program (ILP) has an integrality gap of 2. We propose new cut-based ILP formulations for the problem along with exact branch-and-bound based algorithms. While our new formulations cannot improve the integrality gap, we can prove that one of them yields stronger linear programming bounds than the two previous strongest formulations: The directed cut formulation [Balakrishnan et al., 1989; Chopra and Rao, 1994] and the advanced flow-based formulation by Magnanti and Raghavan [Magnanti and Raghavan, 2005]. In an experimental evaluation, we show that the linear programming bounds of the new formulations are indeed strong on practical instances and that our new branch-and-bound algorithms outperform branch-and-bound algorithms based on the previous formulations. Our formulations can be seen as a cut-based analogon to [Magnanti and Raghavan, 2005], whose existence was an open problem

    Linear Ordering Based MIP Formulations for the Vertex Separation or Pathwidth Problem

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    We consider the vertex separation problem in directed graphs G=(V,A) that has been shown to be equivalent to the pathwidth problem. Naturally, it is modeled as finding a linear order (permutation) of the vertices V such that its induced maximum vertex separation is minimum. Mixed-integer programs proposed so far construct linear orders using either position or set assignment variables. We prove that, for any directed graph, solving their linear programming relaxations yields a lower bound of zero on the true vertex separation number. We then present a new and compact mixed-integer program that sustains stronger lower bounds. It is based on true linear ordering variables and a slightly different perspective on the problem. An experimental evaluation of three formulations in total, each representing a different modeling scheme, displays their potentials and limitations when used to solve the problem to optimality

    Compact Linearization for Binary Quadratic Problems subject to Linear Equations

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    In this paper it is shown that the compact linearization approach, that has been previously proposed only for binary quadratic problems with assignment constraints, can be generalized to arbitrary linear equations with positive coefficients which considerably enlarges its applicability. We discuss special cases of prominent quadratic combinatorial optimization problems where the obtained compact linearization yields a continuous relaxation that is provably as least as strong as the one obtained with an ordinary linearization

    Compact Linearization for Binary Quadratic Problems subject to Assignment Constraints

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    We prove new necessary and sufficient conditions to carry out a compact linearization approach for a general class of binary quadratic problems subject to assignment constraints as it has been proposed by Liberti in 2007. The new conditions resolve inconsistencies that can occur when the original method is used. We also present a mixed-integer linear program to compute a minimally-sized linearization. When all the assignment constraints have non-overlapping variable support, this program is shown to have a totally unimodular constraint matrix. Finally, we give a polynomial-time combinatorial algorithm that is exact in this case and can still be used as a heuristic otherwise

    Crossing Minimization in Storyline Visualization

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    A storyline visualization is a layout that represents the temporal dynamics of social interactions along time by the convergence of chronological lines. Among the criteria oriented at improving aesthetics and legibility of a representation of this type, a small number of line crossings is the hardest to achieve. We model the crossing minimization in the storyline visualization problem as a multi-layer crossing minimization problem with tree constraints. Our algorithm can compute a layout with the minimum number of crossings of the chronological lines. Computational results demonstrate that it can solve instances with more than 100 interactions and with more than 100 chronological lines to optimality

    Exact Integer Programming Approaches to Sequential Instruction Scheduling and Offset Assignment

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    The dissertation at hand presents the main concepts and results derived when studying the optimal solution of two NP-hard compiler optimization problems, namely instruction scheduling and offset assignment, by means of integer programming. It is the outcome of several years of research as an assistant at Michael Jünger's computer science chair in Cologne, with the particular aim to apply exact mathematical optimization techniques to real-world problems arising in the domain of technical computer science. The two problems studied are rather unrelated apart from the fact that they both take place during the machine code generation phase of a compiler and deal with the handling of limited resources. Instruction scheduling is about the assignment of issue clock cycles to instructions in the presence of precedence, latency, and resource constraints such that the total time needed to execute all the instructions is minimized. Offset assignment deals with storage layouts of program variables and the efficient use of address registers for accesses to these variables. The objective is to employ specialized instructions in order to minimize the overhead caused by address computations. While instruction scheduling needs to be carried out by almost every present compiler irrespective of the processor architecture, the offset assignment problem occurs mainly in compilers for highly specialized processor designs. Instruction scheduling is a well-studied field where several exact and heuristic approaches have been developed and experimentally evaluated in the past. In this thesis, we concentrate on the basic-block instruction scheduling problem for single-issue processors. Basic blocks are program fragments with no side-entrances and -exits, i.e., every instruction of a basic block needs to be executed before the control flow may leave it and enter another basic block. Single-issue processors are capable of starting the execution of exactly one instruction per clock cycle. A number of techniques to preprocess instances of the basic-block instruction scheduling problem were proposed in the literature and are, with emphasis on the more recent ones that arose since the year 2000, thoroughly reviewed in this thesis. They finally led to a constraint programming approach in 2006 that was shown to solve about 350,000 instances to optimality and where some of these instances comprised up to about 2,500 instructions. The last attempt to tackle the problem using integer programming however dates to a time prior to the publication of the latest preprocessing advances. While being successful on a set of instances that impose very restrictive latency constraints, it was shown to be unable to solve hundreds of instances from the aforementioned benchmark set that comprises also large and varying latencies. In addition, the previous integer programming models were almost all based on so-called time-indexed formulations where decision variables model an explicit assignment of instructions to clock cycles. In this thesis, a completely different and novel approach is taken based on the linear ordering problem, a well-studied combinatorial optimization problem. The new models lead to alternative characterizations of the feasible solutions to the basic-block instruction scheduling problem. These facilitate the employment of advanced integer programming methodologies, in particular the design of branch-and-cut algorithms that can handle larger instances. The formulations are further extended by additional inequalities that can be used as cutting planes. Combined with the preprocessing routines that are partially extended and improved as well, the respective solver implementation eventually turned out to be competitive to the constraint programming method. Reaching this point has taken some years and this thesis presents not only the derived models but also several ideas and byproducts that arose in the meantime, and that can help and inspire researchers even if they aim at the application of different solution methodologies. The starting point regarding the offset assignment problem was a different one because especially exact solution approaches were rather rare prior to the models presented in this thesis. The offset assignment problem arose in the 1990s and is considered in several variants that are of theoretical and practical interest. In the simplest one, a processor is assumed to provide only a single address register and only very restricted possibilities to avoid address computation overhead. However, even this simplest variant, that may serve as a building block for the more complex ones, is already NP-hard and has been studied mainly from a heuristic point of view. The few existing exact solution approaches were not capable to solve moderately sized instances so that the quality of heuristic solutions relative to the optimum was hardly known at all. Again, the inspection of the combinatorial structure of the various problem variants turned out to be the key for designing branch-and-cut implementations that can profit from knowledge about related combinatorial optimization problems. The implementation targeting the simple problem variant was the first capable to optimally solve the majority of about 3,000 instances collected in a standard benchmark set. The method could then be further generalized in two steps. First, in a collaboration with Roberto Castañeda Lozano, additional techniques could be incorporated into the approach in order to handle multiple address registers. Fortunately, the methods could then even be further extended to as well deal with more flexible addressing capabilities. In this way, the thesis at hand does not only answer the question how large the address computation overhead can be when using heuristics, but as well presents first results that allow to analyze the impact of the mentioned increased addressing capabilities on the runtime performance and size of real-world programs

    2-Layer Fan-Planarity: From Caterpillar to Stegosaurus

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    In a fan-planar drawing of a graph there is no edge that crosses two other independent edges. We study 2-layer fan-planar drawings, i.e., fan-planar drawings such that the vertices are assigned to two distinct horizontal layers and edges are straight-line segments that connect vertices of different layers. We characterize 2-layer fan-planar drawable graphs and describe a linear-time testing and embedding algorithm for biconnected graphs. We also study the relationship between 2-layer fan-planar graphs and 2-layer right-angle crossing graphs

    More General Optimal Offset Assignment

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    This manuscript presents exact approaches to the general offset assignment problem arising in the address code generation phase of compilers for application-specific processors. First, integer programming models for architecture-dependent and theoretically motivated special cases of the problem are established. Then, these models are extended to provide the first widely applicable formulations for the most general problem setting, supporting processors with several address registers and complex addressing capabilities. Existing heuristics are similarly extended and practical applicability of the proposed methods is demonstrated by experimental evaluation using an established and large benchmark set. The experiments allow us to study the impact of exploiting more complex memory addressing capabilities on the address computation costs of real-world programs. We also show how to integrate operand reordering techniques for commutative instructions into existing solution approaches

    A robust schedule for Montpellier's Tramway network

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    The city of Montpellier in the Languedoc-Roussillon region of France features a fast growing tram network as a central part of its public service infrastructure. Here, as in many other tram networks, resources like tracks and stations are shared between different lines. Because of the resulting dependencies, small inevitable delays can spread through the network and affect its global performance. Abstract This article examines whether a robust tram schedule may help to raise punctuality in Montpellier's tram network. To accomplish this, we apply a tool set designed to generate schedules optimized for robustness, which also satisfy given sets of planning requirements. These tools allow to compare time tables with respect to their punctuality and other key indicators. Abstract After an introduction to the goals of this paper, we continue with a description of the tool set focusing on optimization and simulation modules. These software utilities are then employed to generate and simulate robust and non-robust schedules for Montpellier's tram network, which are subsequently compared for the resulting delays

    On bounding the difference of the maximum degree and clique number

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    For every k ∈ ℕ0, we consider graphs in which for any induced subgraph, Δ ≤ ω−1+k holds, where Δ is the maximum degree and ω is the maximum clique number of the subgraph. We give a finite forbidden induced subgraph characterization for every k. As an application, we find some results on the chromatic number χ of a graph. B.Reed stated the conjecture that for every graph, χ ≤ ⌈Δ+ω+1 / 2⌉ holds. Since this inequality is fulfilled by graphs in which Δ ≤ ω+2 holds, our results provide a hereditary graph class for which the conjecture holds

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