26,678 research outputs found

    A Local-to-Global Theorem for Congested Shortest Paths

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    Amiri and Wargalla (2020) proved the following local-to-global theorem in directed acyclic graphs (DAGs): if GG is a weighted DAG such that for each subset SS of 3 nodes there is a shortest path containing every node in SS, then there exists a pair (s,t)(s,t) of nodes such that there is a shortest stst-path containing every node in GG. We extend this theorem to general graphs. For undirected graphs, we prove that the same theorem holds (up to a difference in the constant 3). For directed graphs, we provide a counterexample to the theorem (for any constant), and prove a roundtrip analogue of the theorem which shows there exists a pair (s,t)(s,t) of nodes such that every node in GG is contained in the union of a shortest stst-path and a shortest tsts-path. The original theorem for DAGs has an application to the kk-Shortest Paths with Congestion cc ((k,ck,c)-SPC) problem. In this problem, we are given a weighted graph GG, together with kk node pairs (s1,t1),…,(sk,tk)(s_1,t_1),\dots,(s_k,t_k), and a positive integer c≤kc\leq k. We are tasked with finding paths P1,…,PkP_1,\dots, P_k such that each PiP_i is a shortest path from sis_i to tit_i, and every node in the graph is on at most cc paths PiP_i, or reporting that no such collection of paths exists. When c=kc=k the problem is easily solved by finding shortest paths for each pair (si,ti)(s_i,t_i) independently. When c=1c=1, the (k,c)(k,c)-SPC problem recovers the kk-Disjoint Shortest Paths (kk-DSP) problem, where the collection of shortest paths must be node-disjoint. For fixed kk, kk-DSP can be solved in polynomial time on DAGs and undirected graphs. Previous work shows that the local-to-global theorem for DAGs implies that (k,c)(k,c)-SPC on DAGs whenever k−ck-c is constant. In the same way, our work implies that (k,c)(k,c)-SPC can be solved in polynomial time on undirected graphs whenever k−ck-c is constant.Comment: Updated to reflect reviewer comment

    Computing Motion Plans for Assembling Particles with Global Control

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    We investigate motion planning algorithms for the assembly of shapes in the \emph{tilt model} in which unit-square tiles move in a grid world under the influence of uniform external forces and self-assemble according to certain rules. We provide several heuristics and experimental evaluation of their success rate, solution length, runtime, and memory consumption.Comment: 20 pages, 12 figure

    Implicit Loss of Surjectivity and Facial Reduction: Theory and Applications

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    Facial reduction, pioneered by Borwein and Wolkowicz, is a preprocessing method that is commonly used to obtain strict feasibility in the reformulated, reduced constraint system. The importance of strict feasibility is often addressed in the context of the convergence results for interior point methods. Beyond the theoretical properties that the facial reduction conveys, we show that facial reduction, not only limited to interior point methods, leads to strong numerical performances in different classes of algorithms. In this thesis we study various consequences and the broad applicability of facial reduction. The thesis is organized in two parts. In the first part, we show the instabilities accompanied by the absence of strict feasibility through the lens of facially reduced systems. In particular, we exploit the implicit redundancies, revealed by each nontrivial facial reduction step, resulting in the implicit loss of surjectivity. This leads to the two-step facial reduction and two novel related notions of singularity. For the area of semidefinite programming, we use these singularities to strengthen a known bound on the solution rank, the Barvinok-Pataki bound. For the area of linear programming, we reveal degeneracies caused by the implicit redundancies. Furthermore, we propose a preprocessing tool that uses the simplex method. In the second part of this thesis, we continue with the semidefinite programs that do not have strictly feasible points. We focus on the doubly-nonnegative relaxation of the binary quadratic program and a semidefinite program with a nonlinear objective function. We closely work with two classes of algorithms, the splitting method and the Gauss-Newton interior point method. We elaborate on the advantages in building models from facial reduction. Moreover, we develop algorithms for real-world problems including the quadratic assignment problem, the protein side-chain positioning problem, and the key rate computation for quantum key distribution. Facial reduction continues to play an important role for providing robust reformulated models in both the theoretical and the practical aspects, resulting in successful numerical performances

    Two string theory flavours of generalised Eisenstein series

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    Generalised Eisenstein series are non-holomorphic modular invariant functions of a complex variable, τ\tau, subject to a particular inhomogeneous Laplace eigenvalue equation on the hyperbolic upper-half τ\tau-plane. Two infinite classes of such functions arise quite naturally within different string theory contexts. A first class can be found by studying the coefficients of the effective action for the low-energy expansion of type IIB superstring theory, and relatedly in the analysis of certain integrated four-point functions of stress tensor multiplet operators in N=4\mathcal{N} = 4 supersymmetric Yang-Mills theory. A second class of such objects is known to contain all two-loop modular graph functions, which are fundamental building blocks in the low-energy expansion of closed-string scattering amplitudes at genus one. In this work, we present a Poincar\'e series approach that unifies both classes of generalised Eisenstein series and manifests certain algebraic and differential relations amongst them. We then combine this technique with spectral methods for automorphic forms to find general and non-perturbative expansions at the cusp τ→i∞\tau \to i \infty. Finally, we find intriguing connections between the asymptotic expansion of these modular functions as τ→0\tau \to 0 and the non-trivial zeros of the Riemann zeta function.Comment: 44 pages + 3 figure

    TabR: Unlocking the Power of Retrieval-Augmented Tabular Deep Learning

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    Deep learning (DL) models for tabular data problems are receiving increasingly more attention, while the algorithms based on gradient-boosted decision trees (GBDT) remain a strong go-to solution. Following the recent trends in other domains, such as natural language processing and computer vision, several retrieval-augmented tabular DL models have been recently proposed. For a given target object, a retrieval-based model retrieves other relevant objects, such as the nearest neighbors, from the available (training) data and uses their features or even labels to make a better prediction. However, we show that the existing retrieval-based tabular DL solutions provide only minor, if any, benefits over the properly tuned simple retrieval-free baselines. Thus, it remains unclear whether the retrieval-based approach is a worthy direction for tabular DL. In this work, we give a strong positive answer to this question. We start by incrementally augmenting a simple feed-forward architecture with an attention-like retrieval component similar to those of many (tabular) retrieval-based models. Then, we highlight several details of the attention mechanism that turn out to have a massive impact on the performance on tabular data problems, but that were not explored in prior work. As a result, we design TabR -- a simple retrieval-based tabular DL model which, on a set of public benchmarks, demonstrates the best average performance among tabular DL models, becomes the new state-of-the-art on several datasets, and even outperforms GBDT models on the recently proposed ``GBDT-friendly'' benchmark (see the first figure).Comment: Code: https://github.com/yandex-research/tabular-dl-tab

    Signed tropicalization of polar cones

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    We study the tropical analogue of the notion of polar of a cone, working over the semiring of tropical numbers with signs. We characterize the cones which arise as polars of sets of tropically nonnegative vectors by an invariance property with respect to a tropical analogue of Fourier-Motzkin elimination. We also relate tropical polars with images by the nonarchimedean valuation of classical polars over real closed nonarchimedean fields and show, in particular, that for semi-algebraic sets over such fields, the operation of taking the polar commutes with the operation of signed valuation (keeping track both of the nonarchimedean valuation and sign). We apply these results to characterize images by the signed valuation of classical cones of matrices, including the cones of positive semidefinite matrices, completely positive matrices, completely positive semidefinite matrices, and their polars, including the cone of co-positive matrices, showing that hierarchies of classical cones collapse under tropicalization. We finally discuss an application of these ideas to optimization with signed tropical numbers.Comment: 24 pages, 1 figure. Changes with respect to Version 2: we improved Introduction and added Examples 3.24 and 3.25 illustrating that "bend addition" can be considered as a tropical analogue of the Fourier-Motzkin eliminatio

    Reinforcement learning in large state action spaces

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    Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios. This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory). In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications

    Moments of Dirichlet L-functions in Function Fields

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    In this thesis, we compute several moments and mean values of Dirichlet L-functions in function fields, in both the odd and even characteristic setting.Leverhulme Trus

    Unstable Periodic Orbits: a language to interpret the complexity of chaotic systems

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    Unstable periodic orbits (UPOs), exact periodic solutions of the evolution equation, offer a very powerful framework for studying chaotic dynamical systems, as they allow one to dissect their dynamical structure. UPOs can be considered the skeleton of chaotic dynamics, its essential building blocks. In fact, it is possible to prove that in a chaotic system, UPOs are dense in the attractor, meaning that it is always possible to find a UPO arbitrarily near any chaotic trajectory. We can thus think of the chaotic trajectory as being approximated by different UPOs as it evolves in time, jumping from one UPO to another as a result of their instability. In this thesis we provide a contribution towards the use of UPOs as a tool to understand and distill the dynamical structure of chaotic dynamical systems. We will focus on two models, characterised by different properties, the Lorenz-63 and Lorenz-96 model. The process of approximation of a chaotic trajectory in terms of UPOs will play a central role in our investigation. In fact, we will use this tool to explore the properties of the attractor of the system under the lens of its UPOs. In the first part of the thesis we consider the Lorenz-63 model with the classic parameters’ value. We investigate how a chaotic trajectory can be approximated using a complete set of UPOs up to symbolic dynamics’ period 14. At each instant in time, we rank the UPOs according to their proximity to the position of the orbit in the phase space. We study this process from two different perspectives. First, we find that longer period UPOs overwhelmingly provide the best local approximation to the trajectory. Second, we construct a finite-state Markov chain by studying the scattering of the trajectory between the neighbourhood of the various UPOs. Each UPO and its neighbourhood are taken as a possible state of the system. Through the analysis of the subdominant eigenvectors of the corresponding stochastic matrix we provide a different interpretation of the mixing processes occurring in the system by taking advantage of the concept of quasi-invariant sets. In the second part of the thesis we provide an extensive numerical investigation of the variability of the dynamical properties across the attractor of the much studied Lorenz ’96 dynamical system. By combining the Lyapunov analysis of the tangent space with the study of the shadowing of the chaotic trajectory performed by a very large set of unstable periodic orbits, we show that the observed variability in the number of unstable dimensions, which shows a serious breakdown of hyperbolicity, is associated with the presence of a substantial number of finite-time Lyapunov exponents that fluctuate about zero also when very long averaging times are considered
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