Matching In General Graphs Research Articles

Learnable Graph Matching: A Practical Paradigm for Data Association.

Data association is at the core of many computer vision tasks, e.g., multiple object tracking, image matching, and point cloud registration. however, current data association solutions have some defects: they mostly ignore the intra-view context information; besides, they either train deep association models in an end-to-end way and hardly utilize the advantage of optimization-based assignment methods, or only use an off-the-shelf neural network to extract features. In this paper, we propose a general learnable graph matching method to address these issues. Especially, we model the intra-view relationships as an undirected graph. Then data association turns into a general graph matching problem between graphs. Furthermore, to make optimization end-to-end differentiable, we relax the original graph matching problem into continuous quadratic programming and then incorporate training into a deep graph neural network with KKT conditions and implicit function theorem. In MOT task, our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet. For point cloud registration, we also achieve competitive results.

Parallel Maximum Cardinality Matching for General Graphs on GPUs.

The matching problem formulated as Maximum Cardinality Matching in General Graphs (MCMGG) finds the largest matching on graphs without restrictions. The Micali-Vazirani algorithm has the best asymptotic complexity for solving MCMGG when the graphs are sparse. Parallelizing matching in general graphs on the GPU is difficult for multiple reasons. First, the augmenting path procedure is highly recursive, and NVIDIA GPUs use registers to store kernel arguments, which eventually spill into cached device memory, with a performance penalty. Second, extracting parallelism from the matching process requires partitioning the graph to avoid any overlapping augmenting paths. We propose an implementation of the Micali-Vazirani algorithm which identifies bridge edges using thread-parallel breadth-first search, followed by block-parallel path augmentation and blossom contraction. Augmenting path and Union-find methods were implemented as stack-based iterative methods, with a stack allocated in shared memory. Our experimentation shows that compared to the serial implementation, our approach results in up to 15-fold speed-up for very sparse regular graphs, up to 5-fold slowdown for denser regular graphs, and finally a 50-fold slowdown for power-law distributed Kronecker graphs. This implementation has been open-sourced for further research on developing combinatorial graph algorithms on GPUs.

Shared-Memory Parallel Edmonds Blossom Algorithm for Maximum Cardinality Matching in General Graphs.

The Edmonds Blossom algorithm is implemented here using depth-first search, which is intrinsically serial. By streamlining the code, our serial implementation is consistently three to five times faster than the previously fastest general graph matching code. By extracting parallelism across iterations of the algorithm, with coarse-grain locking, we are able to further reduce the run time on random regular graphs four-fold and obtain a two-fold reduction of run time on real-world graphs with similar topology. Solving very sparse graphs (average degree less than four) exhibiting community structure with eight threads led to a slow down of three-fold, but this slow down is replaced by marginal speed up once the average degree is greater than four. We conclude that our parallel coarse-grain locking implementation performs well when extracting parallelism from this augmenting-path-based algorithm and may work well for similar algorithms.

A Theory of Alternating Paths and Blossoms from the Perspective of Minimum Length

The Micali–Vazirani (MV) algorithm for finding a maximum cardinality matching in general graphs, which was published in 1980, remains to this day the most efficient known algorithm for the problem. The current paper gives the first complete and correct proof of this algorithm. The MV algorithm resorts to finding minimum-length augmenting paths. However, such paths fail to satisfy an elementary property, called breadth first search honesty in this paper. In the absence of this property, an exponential time algorithm appears to be called for—just for finding one such path. On the other hand, the MV algorithm accomplishes this and additional tasks in linear time. The saving grace is the various “footholds” offered by the underlying structure, which the algorithm uses in order to perform its key tasks efficiently. The theory expounded in this paper elucidates this rich structure and yields a proof of correctness of the algorithm. It may also be of independent interest as a set of well-knit graph-theoretic facts. Funding: This work was supported in part by the National Science Foundation [Grant CCF-2230414].

Interactive Theorem Provers: Applications in AI, Opportunities, and Challenges

Interactive theorem provers (ITPs) are computer programs in which axioms and a conjecture are stated in a formal language, and a user provides the ITP with relatively high-level steps of a formal proof for the conjecture. Then, by invoking automated theorem provers, the ITP tries to generate low-level steps that fill the gaps between the steps provided by the user, thus forming a complete formal proof of the conjecture. The ITP also checks the entire formal proof against the axioms, thus confirming the soundness of all derivations in the formal proof. In this talk, I will discuss the existing opportunities and potential benefits to applying ITPs to reason about and verify AI concepts, algorithms, and software. I will also discuss the challenges we have to being able to apply ITPs in AI and reap those benefits. I will do so by discussing a number of my previous projects on the application of ITPs to different AI concepts, algorithms, and software systems. These projects span different areas of planning (classical planning, temporal planning, and planning under uncertainty) as well as algorithms with applications in algorithmic game theory, like general graph matching and online matching.

MAXIMUM MATCHINGS IN A PSEUDOFRACTAL SCALE-FREE WEB

The maximum matching problem is of great interest in many areas, for example, statistical physics and theoretical computer science. However, precise determining of maximum matchings in general graphs is a challenge and calculationally intractable. In this paper, we study analytically maximum matching problem in the pseudofractal scale-free web. We derive exact expressions for size and number of maximum matchings of the network based on self-similar structure and iterative construction of network, and obtain asymptotic incremental constant for the number of maximum matchings. The obtained results are helpful to understand the structural characteristics of scale-free networks with pseudofractality.

The general graph matching game: Approximate core

The classic paper of Shapley and Shubik (1971) characterized the core of the assignment game using ideas from matching theory and LP-duality theory and their highly non-trivial interplay. Whereas the core of this game is always non-empty, that of the general graph matching game can be empty.This paper salvages the situation by giving an imputation in the 2/3-approximate core for the latter; moreover this imputation can be computed in polynomial time. This bound is best possible, since it is the integrality gap of the natural underlying LP. Our profit allocation method goes further: the multiplier on the profit of an agent is often better than 23 and lies in the interval [23,1], depending on how severely constrained the agent is.

Using Inter-Mesh Links to Reduce End-to-End Delay in Walker Delta Constellations

In Walker delta constellations, satellites moving in different directions form two meshes. Inter-mesh links that connect satellites in different meshes are typically not used due to high relative speed. However, this hurts the end-to-end delay performance. In this letter, we aim to find a tradeoff between the system complexity of establishing such links and the delay performance. An inter-mesh link scheduling method is proposed by considering multiple constraints, including link distance, angular velocity, link setup time, and link switching frequency. First, for a given set of satellites, a set of candidate inter-mesh links are identified which fulfill link distance and angular velocity constraints and maximize topology duration. The inter-mesh links are selected based on maximum matching in general graphs. Then, a set of satellite positions are identified which prolong the topology duration. Based on these positions, satellites in an orbit alternately establish inter-mesh links to fulfill the link setup time constraint. Simulation reveals the benefits of the proposed method: the average end-to-end delay is reduced significantly with acceptable system overhead.

Approximate Graph Edit Distance in Quadratic Time.

Graph edit distance is one of the most flexible and general graph matching models available. The major drawback of graph edit distance, however, is its computational complexity that restricts its applicability to graphs of rather small size. Recently, the authors of the present paper introduced a general approximation framework for the graph edit distance problem. The basic idea of this specific algorithm is to first compute an optimal assignment of independent local graph structures (including substitutions, deletions, and insertions of nodes and edges). This optimal assignment is complete and consistent with respect to the involved nodes of both graphs and can thus be used to instantly derive an admissible (yet suboptimal) solution for the original graph edit distance problem in O(n3) time. For large scale graphs or graph sets, however, the cubic time complexity may still be too high. Therefore, we propose to use suboptimal algorithms with quadratic rather than cubic time for solving the basic assignment problem. In particular, the present paper introduces five different greedy assignment algorithms in the context of graph edit distance approximation. In an experimental evaluation, we show that these methods have great potential for further speeding up the computation of graph edit distance while the approximated distances remain sufficiently accurate for graph based pattern classification.

The Average Size of Matchings in Graphs

In this paper, we consider the average size of independent edge sets, also called matchings, in a graph. We characterize the extremal graphs for the average size of matchings in general graphs and trees. In addition, we obtain inequalities between the average size of matchings and the number of matchings as well as the matching energy, which is defined as the sum of the absolute values of the zeros of the matching polynomial.

Large rainbow matchings in general graphs

By a theorem of Drisko, any 2n−1 matchings of size n in a bipartite graph have a rainbow matching of size n. Inspired by results and discussion of Barát, Gyárfás and Sárközy, we conjecture that if n is odd then the same is true also in general graphs, and that if n is even then 2n matchings of size n suffice. We prove that any 3n−2 matchings of size n have a rainbow matching of size n.

Enumeration of perfect matchings of lattice graphs by Pfaffians

Enumeration of perfect matchings in general graphs (even in bipartite graphs) is #P-complete. In this paper, we obtain explicit expressions of the number of perfect matchings of 8.6.4 lattices with toroidal boundary by enumerating Pfaffians.

Scaling Algorithms for Weighted Matching in General Graphs

We present a new scaling algorithm for maximum (or minimum) weight perfect matching on general, edge weighted graphs. Our algorithm runs in O ( m √ n log( nN )) time, O ( m √ n ) per scale, which matches the running time of the best cardinality matching algorithms on sparse graphs [16, 20, 36, 37]. Here, m , n , and N bound the number of edges, vertices, and magnitude, respectively, of any integer edge weight. Our result improves on a 25-year-old algorithm of Gabow and Tarjan, which runs in O ( m √ n log n α ( m , n ) log( nN )) time.

An o(n<sup>2.5</sup>) Algorithm: For Maximum Matchings in General Graphs

This article extend the John E. Hopcroft and Richart M. Karp Algorithm (HK Algorithm) for maximum matchings in bipartite graphs to the non-bipartite case by providing a new approach to deal with the blossom in alternating paths in the process of searching for augmenting paths, which different from well-known “shrinking” way of Edmonds and makes the algorithm for maximum matchings in general graphs more simple.

Matching and Weighted [formula omitted]-Packing: Algorithms and Kernels

Parameterized algorithms and kernelization algorithms are presented for the weighted P2-Packing problem, which is a generalization of the famous Graph Matching problem. The parameterized algorithms are based on the following new techniques and observations: (1) new study on structure relationship between graph matchings in general graphs and P2-packings in bipartite graphs; (2) an effective graph bi-partitioning algorithm; and (3) a polynomial-time algorithm for a constrained weighted P2-Packing problem in bipartite graphs. The kernelization algorithms are based on the following new techniques: (1) the application of graph matching in kernelization; (2) a crown reduction structure for weighted problems. These techniques lead to randomized and deterministic parameterized algorithms that significantly improve the previous best upper bounds for the problem for both weighted and unweighted versions. For the kernelization algorithm, by using a weighted version of crown reduction, a kernel of size O(k2) is presented, where k is the given parameter of the problem.

Belief Propagation and LP Relaxation for Weighted Matching in General Graphs

Loopy belief propagation has been employed in a wide variety of applications with great empirical success, but it comes with few theoretical guarantees. In this paper, we analyze the performance of the max-product form of belief propagation for the weighted matching problem on general graphs. We show that the performance of max-product is exactly characterized by the natural linear programming (LP) relaxation of the problem. In particular, we first show that if the LP relaxation has no fractional optima then max-product always converges to the correct answer. This establishes the extension of the recent result by Bayati, Shah and Sharma, which considered bipartite graphs, to general graphs. Perhaps more interestingly, we also establish a tight converse, namely that the presence of any fractional LP optimum implies that max-product will fail to yield useful estimates on some of the edges. We extend our results to the weighted <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</i> -matching and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> -edge-cover problems. We also demonstrate how to simplify the max-product message-update equations for weighted matching, making it easily deployable in distributed settings like wireless or sensor networks.

Computing large matchings fast

In this article we present algorithms for computing large matchings in 3-regular graphs, graphs with maximum degree 3, and 3-connected planar graphs. The algorithms give a guarantee on the size of the computed matching and take linear or slightly superlinear time. Thus they are faster than the best-known algorithm for computing maximum matchings in general graphs, which runs in O (√ nm ) time, where n denotes the number of vertices and m the number of edges of the given graph. For the classes of 3-regular graphs and graphs with maximum degree 3, the bounds we achieve are known to be best possible. We also investigate graphs with block trees of bounded degree, where the d -block tree is the adjacency graph of the d -connected components of the given graph. In 3-regular graphs and 3-connected planar graphs with bounded-degree 2- and 4-block trees, respectively, we show how to compute maximum matchings in slightly superlinear time.

Computing large matchings in planar graphs with fixed minimum degree

In this paper, we present algorithms that compute large matchings in planar graphs with fixed minimum degree. The algorithms give a guarantee on the size of the computed matching and run in linear time. Thus they are faster than the best known algorithm for computing maximum matchings in general graphs and in planar graphs, which run in O ( n m ) and O ( n 1.188 ) time, respectively. For the class of planar graphs with minimum degree 3, the bounds we achieve are known to be the best possible. Further, we discuss how minimum degree 5 can be used to obtain stronger bounds on the matching size.

Quantum Algorithms for Matching Problems

We present quantum algorithms for the following matching problems in unweighted and weighted graphs with n vertices and m edges: Finding a maximal matching in general graphs in time $O(\sqrt{nm}\log^{2}n)$. Finding a maximum matching in general graphs in time $O(n\sqrt{m}\log^{2}n)$. • Finding a maximum weight matching in bipartite graphs in time $O(n\sqrt{m}N\log^{2}n)$, where N is the largest edge weight. Our quantum algorithms are faster than the best known classical deterministic algorithms for the corresponding problems. In particular, the second result solves an open question stated in a paper by Ambainis and Spalek (Proceedings of STACS’06, pp. 172–183, 2006).

Parallel approximation algorithms for maximum weighted matching in general graphs

The problem of computing a matching of maximum weight in a given edge-weighted graph is not known to be P -hard or in RNC . This paper presents two parallel approximation algorithms for this problem. The first is an RNC -approximation scheme, i.e., an RNC algorithm that computes a matching of weight at least 1−ε times the maximum for any fixed constant ε>0. The other is an NC approximation algorithm achieving an approximation ratio of 1/(2+ε) for any fixed constant ε>0.