Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases

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    819 research outputs found

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

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    We consider the task to compute the pathwidth of a graph which is known to be equivalent to solving the vertex separation problem. The latter is naturally modeled as a linear ordering problem with respect to the vertices of the graph. Present mixed-integer programs for the vertex separation problem express linear orders using either position or set assignment variables. However, as we show, the lower bound on the pathwidth obtained when solving their linear programming relaxations is zero for any directed graph. This is one reason for their limited utility in solving larger instances to optimality. We then present a new formulation that is based on conventional linear ordering variables and a slightly different perspective on the problem. Its relaxation provably delivers non-zero lower bounds for any graph whose pathwidth is non-zero. Further structural results for and extensions to this formulation are discussed. Finally, an experimental evaluation of three mixed-integer programs, each representing one of the different yet existing modeling approaches, displays their potentials and limitations when used to solve the problem to optimality in practice

    A Local-Search Algorithm for Steiner Forest

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    In the Steiner Forest problem, we are given a graph and a collection of source-sink pairs, and the goal is to find a subgraph of minimum total length such that all pairs are connected. The problem is APX-Hard and can be 2-approximated by, e.g., the elegant primal-dual algorithm of Agrawal, Klein, and Ravi from 1995. We give a local-search-based constant-factor approximation for the problem. Local search brings in new techniques to an area that has for long not seen any improvements and might be a step towards a combinatorial algorithm for the more general survivable network design problem. Moreover, local search was an essential tool to tackle the dynamic MST/Steiner Tree problem, whereas dynamic Steiner Forest is still wide open. It is easy to see that any constant factor local search algorithm requires steps that add/drop many edges together. We propose natural local moves which, at each step, either (a) add a shortest path in the current graph and then drop a bunch of inessential edges, or (b) add a set of edges to the current solution. This second type of moves is motivated by the potential function we use to measure progress, combining the cost of the solution with a penalty for each connected component. Our carefully-chosen local moves and potential function work in tandem to eliminate bad local minima that arise when using more traditional local moves. Our analysis first considers the case where the local optimum is a single tree, and shows optimality w.r.t. moves that add a single edge (and drop a set of edges) is enough to bound the locality gap. For the general case, we show how to "project" the optimal solution onto the different trees of the local optimum without incurring too much cost (and this argument uses optimality w.r.t. both kinds of moves), followed by a tree-by-tree argument. We hope both the potential function, and our analysis techniques will be useful to develop and analyze local-search algorithms in other contexts

    Compact Hierarchical Graph Drawings via Quadratic Layer Assignment

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    We propose a new mixed-integer programming formulation that very naturally expresses the layout restrictions of a layered (hierarchical) graph drawing and several associated objectives, such as a minimum total arc length, number of reversed arcs, and width, or the adaptation to a specific drawing area, as a special quadratic assignment problem. Our experiments show that it is competitive to another formulation that we slightly simplify as well

    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

    Compact linearization for binary quadratic problems subject to assignment constraints

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    We introduce and 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 that has been proposed by Liberti (4OR 5(3):231–245, 2007, https://doi.org/10.1007/s10288-006-0015-3). 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 be used as a heuristic otherwise

    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

    Improved mixed-integer programming models for the multiprocessor scheduling problem with communication delays

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    We revise existing and introduce new mixed-integer programming models for the Multiprocessor scheduling problem with communication delays. The basis for both is the identification of two major modeling strategies one of which can be considered ordering-based, and the other assignment-based. We first reveal redundancies in the encoding of feasible solutions found in present formulations and discuss how they can be avoided. For the assignment-based approach, we propose new inequalities that lead to provably stronger continuous relaxations and better performance in practice. Moreover, we derive a third, novel modeling strategy and show how to more compactly linearize assignment formulations with quadratic constraints. In a comprehensive experimental comparison of representative models that reflect the state-of-the-art in terms of strength and size, we evaluate not only running times but also the obtained lower and upper bounds on the makespan for the harder instances of a large scale benchmark set

    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

    MIP Formulations for the Steiner Forest Problem

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    The Steiner Forest problem is among the fundamental network design problems. Finding tight linear programming bounds for the problem is the key for both fast Branch-and-Bound algorithms and good primal-dual approximations. On the theoretical side, the best known bound can be obtained from an integer program [KLSv08]. It guarantees a value that is a (2-eps)-approximation of the integer optimum. On the practical side, bounds from a mixed integer program by Magnanti and Raghavan [MR05] are very close to the integer optimum in computational experiments, but the size of the model limits its practical usefulness. We compare a number of known integer programming formulations for the problem and propose three new formulations. We can show that the bounds from our two new cut-based formulations for the problem are within a factor of 2 of the integer optimum. In our experiments, the formulations prove to be both tractable and provide better bounds than all other tractable formulations. In particular, the factor to the integer optimum is much better than 2 in the experiments

    Improved Mixed-Integer Programming Models for Multiprocessor Scheduling with Communication Delays

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    We revise existing and introduce new mixed-integer programming models for the Multiprocessor Scheduling Problem with Communication Delays. At first, we show how to provably reduce the number of product variables necessary to explicitly linearize the so-called packing formulation that contains bilinear terms. Then, we reveal that the feasible region of almost all existing formulations contains redundant solutions and formulate new constraints in order to exclude these. At the same time, by exploiting further structural properties, the models are improved concerning their size, strength, and modeling complexity. The discussion of these improvements leads to new much more compact formulations which are then experimentally compared with each other and with other formulations from the literature. We set up a realistic scenario with a preprocessing of the task graphs, delivering the gained information equally to all the tested models and evaluate not only running times but also the obtained lower and upper bounds on the makespan objective for unsolved instances of a large scale benchmark set

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