A well-known result **of** Grötschel, Lovász and Schrijver [ 39 ] states that – in order to derive a polynomial-time algorithm for solving ( 1.1 ) – the systems Ax ≤ b actually do not need to have small size but are only required to yield a polynomial-time algorithm to solve a certain separation problem. However, using such a description amounts to im- plementing separation routines rather than using **linear** programming solvers in a black-box way. In some sense, this is incompatible with an important aspect **of** the success **of** many mathematical program- ming frameworks, namely the ability to easily formulate problems and use existing algorithms and software – even as a non-mathematician. Therefore, in this work we restrict our attention to formulations that have small size and hence potentially can be easily written down explic- itly. Here, “small” is always understood relatively to the cardinality **of** E and usually means “polynomial”.

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conditions: z(S) ≤ σ(S) for all S ⊆ N .
Let us say a few words about the computational aspects for this problem. A computational problem is defined by its “inputs” and the solution to be computed. The input for a cooperative game includes the set N and the function ν. As ν is defined on 2 N , it requires in general 2 |N | values, one for each subset S ⊆ N . The input size will already be an exponential function in the number **of** players. Under such circumstance, any solution concept (unless the very trivial ones) would require time exponential in the number **of** players. This is true for the core. To evaluate whether an allocation satisfies the sub-group rationality constraints **of** the core requires an exponential number **of** **linear** inequalities. In many practical settings however, such as in the models we are going to introduce, such a high number is not necessary (at least for the input).

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Although tolerance and bounded tolerance graphs have been studied extensively, the recognition problems for both these classes have been the most fundamental open prob- lems since their introduction in 1982 [ 27 , 57 , 62 ]. Therefore, all existing algorithms assume that, along with the input tolerance graph, a tolerance representation **of** it is given. The only result about the complexity **of** recognizing tolerance and bounded toler- ance graphs is that they have a (non-trivial) polynomial sized tolerance representation, hence the problems **of** recognizing tolerance and bounded tolerance graphs are in the class NP [ 66 ]. Recently, a **linear** time recognition algorithm for the subclass **of** bipar- tite tolerance graphs has been presented in [ 27 ]. Furthermore, the class **of** trapezoid graphs (which strictly contains parallelogram, i.e. bounded tolerance, graphs [ 103 ]) can be also recognized in polynomial time [ 90 , 107 ]. On the other hand, the recognition **of** max-tolerance graphs is known to be NP-hard [ 75 ]. Unfortunately, the structure **of** max-tolerance graphs differs significantly from that **of** tolerance graphs (max-tolerance graphs are not even perfect, as they can contain induced C 5 ’s [ 75 ]), so the technique

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f without losing non-negativity or violating any capacities. More on flows can be found in [17]. The reason why we are particularly interested in network flows is that there are efficient combi- natorial algorithms for **linear** **optimization** problems over flow polyhedra. This means that there are algorithms that solve such problems in polynomial time. We assume that the reader has basic knowledge in complexity theory to relate to the terms NP-complete and polynomial time solv- able. If this is not the case, we refer the interested reader to [18] and we give the following rough distinction for the practitioner. If a problem is NP-complete then it is unlikely that we find an efficient algorithm that solves the problem exactly, i.e. we can solve only small instances in practice. If it is polynomial time solvable, then there is an algorithm that solves such a problem within a running time polynomially bounded in the size **of** the input, and we may hope to come up with an algorithm in practice that exactly solves much larger instances in a timely manner. We will describe the bounds for the running times and also the space consumptions with the so-called O-notation. Let f, g : N → R functions from the natural numbers to the reals. The

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This paper presents a branch-and-price-and-cut algorithm for the exact solution **of** the active-passive vehicle-routing problem (APVRP). The APVRP covers a range **of** logistics applications where pickup-and-delivery requests necessitate a joint op- eration **of** active vehicles (e.g., trucks) and passive vehicles (e.g., loading devices such as containers or swap bodies). The objective is to minimize a weighted sum **of** the total distance traveled, the total completion time **of** the routes, and the num- ber **of** unserved requests. To this end, the problem supports a flexible coupling and decoupling **of** active and passive vehicles at customer locations. Accordingly, the operations **of** the vehicles have to be synchronized carefully in the planning. The contribution **of** the paper is twofold: Firstly, we present an exact branch-and- price-and-cut algorithm for this class **of** routing problems with synchronization con- straints. To our knowledge, this algorithm is the first such approach that considers explicitly the temporal interdependencies between active and passive vehicles. The algorithm is based on a non-trivial network representation that models the logical relationships between the different transport tasks necessary to fulfill a request as well as the synchronization **of** the movements **of** active and passive vehicles. Sec- ondly, we contribute to the development **of** branch-and-price methods in general, in that we solve, for the first time, an ng-path relaxation **of** a pricing problem with **linear** vertex costs by means **of** a bidirectional labeling algorithm. Computational experiments show that the proposed algorithm delivers improved bounds and solu- tions for a number **of** APVRP benchmark instances. It is able to solve instances with up to 76 tasks, 4 active, and 8 passive vehicles to optimality within two hours **of** CPU time.

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The degree-two case is **of** special interest due to its relation to the QMST-problem. Combining the **descriptions** **of** higher order spanning tree polytopes with one degree-two monomial for all possible degree- two monomials, we obtain a relaxation **of** the quadratic spanning tree polytope. Doing this with our extended formulations for one degree- two monomial we model in an implicit way a further relation between the monomials and improve the relaxation compared to those we ob- tain using the **descriptions** in the original space. As a side effect, we find new facets **of** the adjacent quadratic forest polytope and the adja- cent quadratic spanning tree polytope. Via computational experiments we visualize the amount **of** improvement **of** the relaxations.

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The topic **of** deriving **linear** characterizations for **combinatorial** optimiza- tion problems via dynamic programs, has been paid some attention, mainly in the 1980’s and 1990’s. Prodon, Liebling, and Groflin [71] give a dy- namic programming based polyhedral characterization for Steiner trees on directed series-parallel graphs (see also Goemans [43]). Barany, Van Roy, and Wolsey [8], Eppen and Martin [30], and Martin, Rardin, and Camp- bell [60] provide such formulations for various kinds **of** lot sizing problems. Further examples are Martin et al. [60] for k-terminal graphs, Liu [57] for 2- terminal Steiner trees, and Raffensperger [72] for the cutting stock, the tank scheduling, and the traveling salesman problem. Recently, Kaibel and Loos (personal communication) provided a dynamic programming based extended formulation for full orbitopes.

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where x denotes the real vector **of** unknown parameters (the point movements) and v denotes the real vector **of** unknown residuals, that is, the degree **of** constraint satisfaction. Both, A (referred to as the design matrix) and l (the vector **of** observations) need to be speciﬁed in advance to deﬁne the constraints. The constraints are perfectly satisﬁed if v = 0. As this is generally not possible for all constraints, the function v T ·P ·v is minimized, where P deﬁnes the weights between diﬀerent constraints. If there are non-**linear** constraints, these are usually replaced by their **linear** approximations. Sarjakoski & Kilpel¨ ainen (1999) and Harrie & Sarjakoski (2002) show how to solve the problem for large datasets, also considering other generalization operators. Applying the same adjustment technique, Koch & Heipke (2005) and Koch (2007) additionally show how to cope with hard inequality constraints that are needed to ensure consistency between DLMs and digital terrain models. Related problems are discussed in the generalization domain, for example, a river must not run uphill (Gaﬀuri, 2007). Least squares adjustment allows diﬀerent generalization operators to be handled, yet the existing generalization methods that are based on this technique do not take the discrete nature **of** map generalization into account. Usually, continuous variables are used to model a problem. These are not suited, for example, to represent whether a vertex **of** an original line is selected for its simpliﬁcation. In their system, Sarjakoski & Kilpel¨ ainen (1999) deﬁne a constraint that attempts to pull an unwanted vertex onto the line connecting its predecessor and successor. This is a smart workaround to also allow for line simpliﬁcation, but **of** course it is not a solution to the discrete problem **of** vertex selection, which only allows two stages and none in between. Sester (2005) applies adjustment calculus to satisfy constraints in building simpliﬁcation, but also points out that it does not solve the whole problem: the elimination **of** details is done in a ﬁrst step, which is not based on **optimization**. The handling **of** hard constraints in **optimization** approaches is seldom addressed in the map generalization literature. Often constraints are relaxed, as they are conﬂicting (Harrie & Weibel, 2007). A few exceptions exist in the context **of** discrete **optimization**, which is addressed in the next section.

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In general there is a certain degree **of** freedom to distribute the vertex delay to different branches **of** the tree. In this delay model we consider only binary Steiner trees where Steiner points can have the same position. By inserting a gate at a vertex **of** the tree it is possible to reduce the delay **of** one **of** the incident branches while increasing the delay on the other branch by about the same amount. As there are only a discrete number **of** gates with different **sizes** available, this effect can be modeled by so-called L 0 (k)-trees for some appropriate k ∈ N where an L 0 (k)-tree is a binary tree in which all edges have positive integral lengths and the sum **of** the lengths **of** the two edges leading from every non-leaf to its two children is k. Then the required arrival times at each sink correspond to a depth restriction for a leaf **of** the

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a tour visiting each vertex exactly once (or Hamiltonian cycle) with minimum total cost **of** its edges. The approach **of** Dantzig, Fulkerson and Johnson was iterative. They first decided on a **Linear** Programming formulation whose optimal solution would provide a lower bound to the length **of** the optimal tour. Due to the exponential size **of** the formulation, its solution would not be computationally feasible. Hence, only a considerably smaller **Linear** Program, containing a subset **of** the constraints, would actually be solved by the Simplex method. If the solution to the LP were found to violate some **of** the constraints which had been omitted, those con- straints would be added to the **Linear** Program, and thus, an iterative procedure would generate successively better lower bounds on the length **of** the optimal tour.

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This last chapter is devoted to the numerical treatment **of** some shape **optimization** prob- lems employing the material from Chapter 6 . We provide a numerical validation that the volume expression allows very accurate approximations and can even reconstruct domains with corners. We begin with numerical results for simple unconstrained domain integrals. Subsequently, we present numerics for the transmission problems from Section 5.2 , Subsec- tion 6.5.1 and the EIT problem **of** Section 5.3 . Except for the example from Section 5.2 , where we use the boundary expression and basis splines, all computations use the domain expression. We discretise the arising partial differential equations by means **of** the finite el- ement method. All implementations in this chapter have been done either using the WIAS toolbox PDELib or the FENICS finite element toolbox. The author acknowledges here the implementation **of** the example from Section 7.2 by Martin Eigel and the level set method **of** Section 7.3 by Antoine Laurain.

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(“9”) **of** phenylene units **of** the CPP ring. Ellipsoidal-shaped CPPs were observed for [9]CPP [126] and [3]CHPB [110] where no or significantly less sterical hindrance **of** phenyl substituents is present. In addition, even numbered CPPs display circular structures. [87] The ellipsoidal shape can be explained by a repulsion-induced alternating twisting **of** the phenylene units. Generally, neighboring phenylenes point in an alternating pattern to the outside and inside **of** the ring to minimize repulsion **of** hydrogen atoms. For an even- numbered ring, this positioning is feasible without inducing additional ring strain. [87, 127, 146] In the case **of** odd-numbered rings, however, one phenylene moiety experiences significantly higher sterical repulsion, as the in- and outside positions (ortho-positions) are blocked by the neighbouring phenylenes (see Figure 18). As a consequence, the entire macrocycle is distorted to minimize this repulsion. Therefore, not a circular but rather an ellipsoidal structure is obtained. The tetraphenylbenzene and biphenyl moieties attached to the CPP ring exert additional strain, since they cannot rotate freely and experience mutual repulsion. Thus, the odd-number **of** phenylene rings and conjunction with sterical crowdedness induces a strong twist **of** phenylene units, which in turn, because **of** the twisting, does result in reduced flexibility.

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On an abstract level, arithmetic matroids oﬀer an abstract theory supporting some notable properties **of** the arithmetic Tutte polynomial, while matroids over rings are a very general and strongly algebraic theory with diﬀerent applications for suitable choices **of** the “base ring” (e.g., to tropical geometry for matroids over discrete valuation rings). However, outside the case **of** lists **of** integer vectors in abelian groups, the arithmetic Tutte polynomial and arithmetic matroids have few **combinatorial** interpretations. For instance, the poset **of** connected com- ponents **of** intersections **of** a toric arrangement – which provides **combinatorial** interpretations for many an evaluation **of** arithmetic Tutte polynomials – has no counterpart in the case **of** non-realizable arithmetic matroids. Moreover, from a structural point **of** view it is striking (and unusual for matroidal objects) that there is no known cryptomorphism for arithmetic matroids, while for matroids over a ring a single one was recently presented [46]. In addition, some conceptual relationships between arithmetic matroids (which come in diﬀerent variants, see [20, 28]) and matroids over rings are not yet cleared.

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Abstract In this paper, we analyze the power con- sumption **of** different GPU-accelerated iterative solver implementations enhanced with energy-saving techni- ques. Specifically, while conducting kernel calls on the graphics accelerator, we manually set the host system to a power-efficient idle-wait status so as to leverage dynamic voltage and frequency control. While the us- age **of** iterative refinement combined with mixed preci- sion arithmetic often improves the execution time **of** an iterative solver on a graphics processor, this may not necessarily be true for the power consumption as well. To analyze the trade-off between computation time and power consumption we compare a plain GMRES solver and its preconditioned variant to the mixed-precision iterative refinement implementations based on the re- spective solvers. Benchmark experiments conclusively reveal how the usage **of** idle-wait during GPU-kernel calls effectively leverages the power-tools provided by hardware, and improves the energy performance **of** the algorithm.

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Elytres allongcies, parall6les, reliord6cs, plus larges que le prouo- notum ; une taclie briine sur la sulure, agant la forme d'un triangle aigu : le grand c0tit en [r]

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The first **of** the two main results in this paper is that there is an algorithm that produces a canonical basis for the spaces **of** D-type that is dual to the canonical basis for the spaces **of** P-type. Here, canonical means that the basis we obtain only depends on the order **of** the elements in the list X and not on any further choices. The two previously known algorithms that construct a basis for spaces **of** D-type depend on additional choices [25, 32]. Our second main result is that far more general pairs **of** zonotopal spaces with nice properties can be constructed than the ones that were previously known. We will define a new **combinatorial** structure called forward ex- change matroid. A forward exchange matroid is an ordered matroid together with a subset **of** its set **of** bases that satisfies a weak version **of** the basis ex- change axiom. This is the underlying structure **of** the generalised zonotopal D-spaces and P-spaces that we introduce.

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Software related speed-up strategies Model specific Solver related Software related speed-up approaches Solver Solver parameters Algorithm Exact methods Meta-Heuristics Proble[r]

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group acting on it. We use the same notion **of** an equivariant acyclic matching as in Equivariant Discrete Morse Theory. The relation between acyclic matchings and poset maps with small fibers can also be adapted to the equivariant case, which is proven in this thesis and can also be found in [7]. For the proof **of** the adaption in the equivariant case, it turns out that using poset maps with small fibers is a nice replacement for **linear** extensions **of** partial orders.

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packages to accept bids. We investigate whether this decision is influenced by the presence **of** a competing auctioneer vying for the same bidders.
Our main insight, derived in a game-theoretic model, is that a revenue-maximizing auction- eer who faces a competitor may find it optimal to restrict the packages on which he will accept bids, in contrast to a monopolistic seller who, in a comparable setting, would not. The intuition is that, by disallowing bids on some packages, a competing auctioneer can differentiate himself from his competitor and attract a more homogeneous group **of** bidders. If auctioneers benefit from stronger competition between similar bidders (i.e., bidders with similar preferences) as compared to competition between dissimilar bidders, then such differentiation is profitable and can occur in equilibrium. Indeed, we find that such segmentation **of** bidders into homogeneous groups occurs in all our equilibria.

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T h e model (description) of a physical system resulting from V will be called classical if the dual space V* is a Banach lattice in the natural order- ing.. With some abuse of termino[r]