Pareto optimality in house allocation problems

We study Pareto optimal matchings in the context of house allocation problems. We present an O(\sqrt{n}m) algorithm, based on Gales Top Trading Cycles Method, for finding a maximum cardinality Pareto optimal matching, where n is the number of agents and m is the total length of the preference lists. By contrast, we show that the problem of finding a minimum cardinality Pareto optimal matching is NP-hard, though approximable within a factor of 2. We then show that there exist Pareto optimal matchings of all sizes between a minimum and maximum cardinality Pareto optimal matching. Finally, we introduce the concept of a signature, which allows us to give a characterization, checkable in linear time, of instances that admit a unique Pareto optimal matching

Popular matchings

We consider the problem of matching a set of applicants to a set of posts, where each applicant has a preference list, ranking a non-empty subset of posts in order of preference, possibly involving ties. We say that a matching M is popular if there is no matching M' such that the number of applicants preferring M' to M exceeds the number of applicants preferring M to M'. In this paper, we give the first polynomial-time algorithms to determine if an instance admits a popular matching, and to find a largest such matching, if one exists. For the special case in which every preference list is strictly ordered (i.e. contains no ties), we give an O(n+m) time algorithm, where n is the total number of applicants and posts, and m is the total length of all the preference lists. For the general case in which preference lists may contain ties, we give an O(√nm) time algorithm, and show that the problem has equivalent time complexity to the maximum-cardinality bipartite matching problem

Fractal dimension of domain walls in two-dimensional Ising spin glasses

We study domain walls in 2d Ising spin glasses in terms of a minimum-weight path problem. Using this approach, large systems can be treated exactly. Our focus is on the fractal dimension $d_f$ of domain walls, which describes via \simL^{d_f} the growth of the average domain-wall length with %% systems size $L\times L$. %% 20.07.07 OM %% Exploring systems up to L=320 we yield $d_f=1.274(2)$ for the case of Gaussian disorder, i.e. a much higher accuracy compared to previous studies. For the case of bimodal disorder, where many equivalent domain walls exist due to the degeneracy of this model, we obtain a true lower bound $d_f=1.095(2)$ and a (lower) estimate $d_f=1.395(3)$ as upper bound. Furthermore, we study the distributions of the domain-wall lengths. Their scaling with system size can be described also only by the exponent $d_f$, i.e. the distributions are monofractal. Finally, we investigate the growth of the domain-wall width with system size (``roughness'') and find a linear behavior.Comment: 8 pages, 8 figures, submitted to Phys. Rev. B; v2: shortened versio

How to Couple from the Past Using a Read-Once Source of Randomness

We give a new method for generating perfectly random samples from the stationary distribution of a Markov chain. The method is related to coupling from the past (CFTP), but only runs the Markov chain forwards in time, and never restarts it at previous times in the past. The method is also related to an idea known as PASTA (Poisson arrivals see time averages) in the operations research literature. Because the new algorithm can be run using a read-once stream of randomness, we call it read-once CFTP. The memory and time requirements of read-once CFTP are on par with the requirements of the usual form of CFTP, and for a variety of applications the requirements may be noticeably less. Some perfect sampling algorithms for point processes are based on an extension of CFTP known as coupling into and from the past; for completeness, we give a read-once version of coupling into and from the past, but it remains unpractical. For these point process applications, we give an alternative coupling method with which read-once CFTP may be efficiently used.Comment: 28 pages, 2 figure

Four results on randomized incremental constructions

We prove four results on randomized incremental constructions (RICs): \begin{itemize} \item an analysis of the expected behavior under insertion and deletions, \item a fully dynamic data structure for convex hull maintenance in arbitrary dimensions, \item a tail estimate for the space complexity of RICs, \item a lower bound on the complexity of a game related to RICs. \end{itemize

Area-time optimal division for T = Ω((log n)1 + ε)

AbstractArea-time optimal VLSI division circuits are described for all computation times in the range [Ω((log n)1+ε), O(n)] for arbitrary ε > 0

A Static Optimality Transformation with Applications to Planar Point Location

Over the last decade, there have been several data structures that, given a planar subdivision and a probability distribution over the plane, provide a way for answering point location queries that is fine-tuned for the distribution. All these methods suffer from the requirement that the query distribution must be known in advance. We present a new data structure for point location queries in planar triangulations. Our structure is asymptotically as fast as the optimal structures, but it requires no prior information about the queries. This is a 2D analogue of the jump from Knuth's optimum binary search trees (discovered in 1971) to the splay trees of Sleator and Tarjan in 1985. While the former need to know the query distribution, the latter are statically optimal. This means that we can adapt to the query sequence and achieve the same asymptotic performance as an optimum static structure, without needing any additional information.Comment: 13 pages, 1 figure, a preliminary version appeared at SoCG 201

A Combinatorial Polynomial Algorithm for the Linear {Arrow-Debreu} Market

We present the first combinatorial polynomial time algorithm for computing the equilibrium of the Arrow-Debreu market model with linear utilities

Improved Balanced Flow Computation Using Parametric Flow

We present a new algorithm for computing balanced flows in equality networks arising in market equilibrium computations. The current best time bound for computing balanced flows in such networks requires $O(n)$ maxflow computations, where $n$ is the number of nodes in the network [Devanur et al. 2008]. Our algorithm requires only a single parametric flow computation. The best algorithm for computing parametric flows [Gallo et al. 1989] is only by a logarithmic factor slower than the best algorithms for computing maxflows. Hence, the running time of the algorithms in [Devanur et al. 2008] and [Duan and Mehlhorn 2015] for computing market equilibria in linear Fisher and Arrow-Debreu markets improve by almost a factor of $n$

Runtime prediction of real programs on real machines

Algorithms are more and more made available as part of libraries or tool kits. For a user of such a library statements of asymptotic running times are almost meaningless as he has no way to estimate the constants involved. To choose the right algorithm for the targeted problem size and the available hardware, knowledge about these constants is important. Methods to determine the constants based on regression analysis or operation counting are not practicable in the general case due to inaccuracy and costs respectively. We present a new general method to determine the implementation and hardware specific running time constants for combinatorial algorithms. This method requires no changes of the implementation of the investigated algorithm and is applicable to a wide range of of programming languages. Only some additional code is necessary. The determined constants are correct within a constant factor which depends only on the hardware platform. As an example the constants of an implementation of a hierarchy of algorithms and data structures are determined. The hierarchy consists of an algorithm for the maximum weighted bipartite matching problem (MWBM), Dijkstra's algorithm, a Fibonacci heap and a graph representation based on adjacency lists. ion frequencies are at most 50 \% on the tested hardware platforms