IA Scholar Query: More algorithms for all-pairs shortest paths in weighted graphs.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgSat, 01 Oct 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Descriptive Combinatorics and Distributed Algorithms
https://scholar.archive.org/work/pjgjlnfrkzd5vmd7p7c5yr66pe
In this article we shall explore a fascinating area called descriptive combinatorics and its recently discovered connections to distributed algorithms-a fundamental part of computer science that is becoming increasingly important in the modern era of decentralized computation. The interdisciplinary nature of these connections means that there is very little common background shared by the researchers who are interested in them. With this in mind, this article was written under the assumption that the reader would have close to no background in either descriptive set theory or computer science. The reader will judge to what degree this endeavor was successful. The article comprises two parts. In the first part we give a brief introduction to some of the central notions and problems of descriptive combinatorics. The second part is devoted to a survey of some of the results concerning theAnton Bernshteynwork_pjgjlnfrkzd5vmd7p7c5yr66peSat, 01 Oct 2022 00:00:00 GMTSwarm Intelligence Technique for Supply Chain Market in Logistic Analytics Management
https://scholar.archive.org/work/3fteicvfdnb4bazlnbr3dbyvw4
Supply chain management has become increasingly important as an academic subject due to globalization developments contributing to massive production-related benefits reallocation. The huge volume of data produced in the global economy means that new tools must be created to manage and evaluate the data and measure organizational performance worldwide. Smart technologies such as swarm intelligence and big data analytics can help get clear data of the location, condition, and environment of products and processes at any time, anywhere to make smart decisions and take corrective schedules that the supply chain can run more effectively. This study proposes the swarm intelligence modeling-based logistic analytics management (SIMLAM) in service supply chain market. A generalized structure for swarm intelligence implementation in supply chain management is suggested, which is advantageous to industry practitioners. Different deterministic methods practically fail due to the intrinsic computational complexity of the problem of higher dimensions.Qian Tian, Qingwei Yin, Yagang Mengwork_3fteicvfdnb4bazlnbr3dbyvw4Sat, 01 Oct 2022 00:00:00 GMTSuitable Site Selection of Water ATMs (Basis of Interior/Exterior Conditions) Using Graph Theory
https://scholar.archive.org/work/gyyikkgbpjdhnjehfnjwdbjf4q
Maintenance performs a vital role in assuring safety operation, enhancing the quality and accumulating the durability of the system. In this paper a method has been evolved to solve the different kinds of issues like new software installation, upgradation of current software, fixing of required equipment, raw water supply problem etc. To continue the service of water ATMs we cannot start maintenance of all the water ATMs together in any particular site, so authors require a proper network planning. Authors have a developed an algorithm to select the best site for fixing of Water ATMs. So individual can use this algorithm and find out the best sites for setting up of Water ATMs such that maximum numbers of persons gain the advantage of Water ATMs. In addition authors have planned an IoT enable Water ATMs. The IoT enabled technology put in the various function of Water ATMs, safeguarding the quality of water.Nayeemuddin Ahmed, Atowar-Ul Islam, Kanak C. Borawork_gyyikkgbpjdhnjehfnjwdbjf4qSat, 01 Oct 2022 00:00:00 GMTMARRS: A Framework for multi-objective risk-aware route recommendation using Multitask-Transformer
https://scholar.archive.org/work/xr5f5qibkfg6rgczqf7iuzd6xq
One of the most significant map services in navigation applications is route recommendation. However, most route recommendation systems only recommend trips based on time and distance, impacting quality-of-experience and route selection. This paper introduces a novel framework, namely MARRS, a multi-objective route recommendation system based on heterogeneous urban sensing open data (i.e., crime, accident, traffic flow, road network, meteorological, calendar event, and point of interest distributions). We introduce a wide, deep, and multitask-learning (WD-MTL) framework that uses a transformer to extract spatial, temporal, and semantic correlation for predicting crime, accident, and traffic flow of particular road segment. Later, for a particular source and destination, the adaptive epsilon constraint technique is used to optimize route satisfying multiple objective functions. The experimental results demonstrate the feasibility of figuring out the safest and efficient route selection.Bhumika, Debasis Daswork_xr5f5qibkfg6rgczqf7iuzd6xqSun, 18 Sep 2022 00:00:00 GMTSub-GMN: The Neural Subgraph Matching Network Model
https://scholar.archive.org/work/7bunlgkuwjfpdir45dhl7ozci4
As one of the most fundamental tasks in graph theory, subgraph matching is a crucial task in many fields, ranging from information retrieval, computer vision, biology, chemistry and natural language processing. Yet subgraph matching problem remains to be an NP-complete problem. This study proposes an end-to-end learning-based approximate method for subgraph matching task, called subgraph matching network (Sub-GMN). The proposed Sub-GMN firstly uses graph representation learning to map nodes to node-level embedding. It then combines metric learning and attention mechanisms to model the relationship between matched nodes in the data graph and query graph. To test the performance of the proposed method, we applied our method on two databases. We used two existing methods, GNN and FGNN as baseline for comparison. Our experiment shows that, on dataset 1, on average the accuracy of Sub-GMN are 12.21\% and 3.2\% higher than that of GNN and FGNN respectively. On average running time Sub-GMN runs 20-40 times faster than FGNN. In addition, the average F1-score of Sub-GMN on all experiments with dataset 2 reached 0.95, which demonstrates that Sub-GMN outputs more correct node-to-node matches. Comparing with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage. Another advantage of our proposed Sub-GMN is that it can output a list of node-to-node matches, while most existing end-to-end GNNs based methods cannot provide the matched node pairs.Zixun Lan, Limin Yu, Linglong Yuan, Zili Wu, Qiang Niu, Fei Mawork_7bunlgkuwjfpdir45dhl7ozci4Fri, 16 Sep 2022 00:00:00 GMTMore Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference
https://scholar.archive.org/work/hh4b7hiz7rdujh4i3y3ytvxqpq
Graph similarity measurement, which computes the distance/similarity between two graphs, arises in various graph-related tasks. Recent learning-based methods lack interpretability, as they directly transform interaction information between two graphs into one hidden vector and then map it to similarity. To cope with this problem, this study proposes a more interpretable end-to-end paradigm for graph similarity learning, named Similarity Computation via Maximum Common Subgraph Inference (INFMCS). Our critical insight into INFMCS is the strong correlation between similarity score and Maximum Common Subgraph (MCS). We implicitly infer MCS to obtain the normalized MCS size, with the supervision information being only the similarity score during training. To capture more global information, we also stack some vanilla transformer encoder layers with graph convolution layers and propose a novel permutation-invariant node Positional Encoding. The entire model is quite simple yet effective. Comprehensive experiments demonstrate that INFMCS consistently outperforms state-of-the-art baselines for graph-graph classification and regression tasks. Ablation experiments verify the effectiveness of the proposed computation paradigm and other components. Also, visualization and statistics of results reveal the interpretability of INFMCS.Zixun Lan, Binjie Hong, Ye Ma, Fei Mawork_hh4b7hiz7rdujh4i3y3ytvxqpqFri, 16 Sep 2022 00:00:00 GMTLearning the Quality of Machine Permutations in Job Shop Scheduling
https://scholar.archive.org/work/hhjdjgdcurgrdkvbfoapyq63be
In recent years, the power demonstrated by Machine Learning (ML) has increasingly attracted the interest of the optimization community that is starting to leverage ML for enhancing and automating the design of algorithms. One combinatorial optimization problem recently tackled with ML is the Job Shop scheduling Problem (JSP). Most of the works on the JSP using ML focus on Deep Reinforcement Learning (DRL), and only a few of them leverage supervised learning techniques. The recurrent reasons for avoiding supervised learning seem to be the difficulty in casting the right learning task, i.e., what is meaningful to predict, and how to obtain labels. Therefore, we first propose a novel supervised learning task that aims at predicting the quality of machine permutations. Then, we design an original methodology to estimate this quality, and we use these estimations to create an accurate sequential deep learning model (binary accuracy above 95%). Finally, we empirically demonstrate the value of predicting the quality of machine permutations by enhancing the performance of a simple Tabu Search algorithm inspired by the works in the literature.Andrea Corsini, Simone Calderara, Mauro Dell'Amicowork_hhjdjgdcurgrdkvbfoapyq63beFri, 16 Sep 2022 00:00:00 GMTOn network backbone extraction for modeling online collective behavior
https://scholar.archive.org/work/c4kf7rrqffaflej7vkayfqas3e
Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. However, only a fraction of edges contribute to the actual investigation. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation.Carlos Henrique Gomes Ferreira, Fabricio Murai, Ana P C Silva, Martino Trevisan, Luca Vassio, Idilio Drago, Marco Mellia, Jussara M Almeidawork_c4kf7rrqffaflej7vkayfqas3eThu, 15 Sep 2022 00:00:00 GMTCultures as networks of cultural traits: A unifying framework for measuring culture and cultural distances
https://scholar.archive.org/work/jeqpgngbc5dwvhg6lovypwlaxa
Making use of the information from the World Value Survey (WVS), and operationalizing a definition of national culture that encompasses both the relevance of specific cultural traits and the interdependence among them, this paper proposes a methodology to reveal the latent structure of national culture and to measure cultural distance between countries that takes into account both the difference in cultural traits and the difference in the network structure of national cultures. Exploiting the possibilities offered by copula graphical models for discrete data, this paper infers the cultural networks of all the countries included in the WVS (Wave 6) and proposes a novel unifying framework to measure national culture and international cultural distances. The Jeffreys' divergence between copula graphical models, taken as the measure of cultural distance between countries, captures the orthogonality of the two components of cultural distance: the one based on cultural traits and the one based on the network structure among them. Moreover, the two components are shown to correlate with different national and structural characteristics of cultural networks, thus encompassing the different informational sets related to national cultures.Luca De Benedictis, Roberto Rondinelli, Veronica Vinciottiwork_jeqpgngbc5dwvhg6lovypwlaxaThu, 15 Sep 2022 00:00:00 GMTTopology of products similarity network for market forecasting
https://scholar.archive.org/work/635q6cokszh2tgyj4l2lhqz3ie
The detection and prediction of risk in financial markets is one of the main challenges of economic forecasting, and draws much attention from the scientific community. An even more challenging task is the prediction of the future relative gain of companies. We here develop a novel combination of product text analysis, network theory and topological based machine learning to study the future performance of companies in financial markets. Our network links are based on the similarity of firms' products and constructed using the Securities Exchange Commission (SEC) filings of US listed firms. We find that several topological features of this network can serve as good precursors of risks or future gain of companies. We then apply machine learning to network attributes vectors for each node to predict successful and failing firms. The resulting accuracies are much better than current state of the art techniques. The framework presented here not only facilitates the prediction of financial markets but also provides insight and demonstrates the power of combining network theory and topology based machine learning.Jingfang Fan, Keren Cohen, Louis M. Shekhtman, Sibo Liu, Jun Meng, Yoram Louzoun, Shlomo Havlin, Technische Informationsbibliothek (TIB)work_635q6cokszh2tgyj4l2lhqz3ieThu, 15 Sep 2022 00:00:00 GMTThe flow tree formula for Donaldson-Thomas invariants of quivers with potentials
https://scholar.archive.org/work/s6n5jncncffodj4unhwqfnkraq
We prove the flow tree formula conjectured by Alexandrov and Pioline which computes Donaldson-Thomas invariants of quivers with potentials in terms of a smaller set of attractor invariants. This result is obtained as a particular case of a more general flow tree formula reconstructing a consistent scattering diagram from its initial walls.Hülya Argüz, Pierrick Bousseauwork_s6n5jncncffodj4unhwqfnkraqThu, 15 Sep 2022 00:00:00 GMT(1+ε)-Approximate Shortest Paths in Dynamic Streams
https://scholar.archive.org/work/6b4zcdf6lnga3eh6esvhhknx5u
Computing approximate shortest paths in the dynamic streaming setting is a fundamental challenge that has been intensively studied. Currently existing solutions for this problem either build a sparse multiplicative spanner of the input graph and compute shortest paths in the spanner offline, or compute an exact single source BFS tree. Solutions of the first type are doomed to incur a stretch-space tradeoff of 2κ-1 versus n^{1+1/κ}, for an integer parameter κ. (In fact, existing solutions also incur an extra factor of 1+ε in the stretch for weighted graphs, and an additional factor of log^{O(1)}n in the space.) The only existing solution of the second type uses n^{1/2 - O(1/κ)} passes over the stream (for space O(n^{1+1/κ})), and applies only to unweighted graphs. In this paper we show that (1+ε)-approximate single-source shortest paths can be computed with Õ(n^{1+1/κ}) space using just constantly many passes in unweighted graphs, and polylogarithmically many passes in weighted graphs. Moreover, the same result applies for multi-source shortest paths, as long as the number of sources is O(n^{1/κ}). We achieve these results by devising efficient dynamic streaming constructions of (1 + ε, β)-spanners and hopsets. On our way to these results, we also devise a new dynamic streaming algorithm for the 1-sparse recovery problem. Even though our algorithm for this task is slightly inferior to the existing algorithms of [S. Ganguly, 2007; Graham Cormode and D. Firmani, 2013], we believe that it is of independent interest.Michael Elkin, Chhaya Trehan, Amit Chakrabarti, Chaitanya Swamywork_6b4zcdf6lnga3eh6esvhhknx5uThu, 15 Sep 2022 00:00:00 GMTShannon meets Myerson: Information Extraction from a Strategic Sender
https://scholar.archive.org/work/dpfwfvl7tvclranstv4w3rydj4
We study a setting where a receiver must design a questionnaire to recover a sequence of symbols known to strategic sender, whose utility may not be incentive compatible. We allow the receiver the possibility of selecting the alternatives presented in the questionnaire, and thereby linking decisions across the components of the sequence. We show that, despite the strategic sender and the noise in the channel, the receiver can recover exponentially many sequences, but also that exponentially many sequences are unrecoverable even by the best strategy. We define the growth rate of the number of recovered sequences as the information extraction capacity. A generalization of the Shannon capacity, it characterizes the optimal amount of communication resources required. We derive bounds leading to an exact evaluation of the information extraction capacity in many cases. Our results form the building blocks of a novel, noncooperative regime of communication involving a strategic sender.Anuj S. Vora, Ankur A. Kulkarniwork_dpfwfvl7tvclranstv4w3rydj4Thu, 15 Sep 2022 00:00:00 GMTÕ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance
https://scholar.archive.org/work/pw2nujktqzf7ldxjjrw2uu4jt4
Computing the edit distance of two strings is one of the most basic problems in computer science and combinatorial optimization. Tree edit distance is a natural generalization of edit distance in which the task is to compute a measure of dissimilarity between two (unweighted) rooted trees with node labels. Perhaps the most notable recent application of tree edit distance is in NoSQL big databases, such as MongoDB, where each row of the database is a JSON document represented as a labeled rooted tree, and finding dissimilarity between two rows is a basic operation. Until recently, the fastest algorithm for tree edit distance ran in cubic time (Demaine, Mozes, Rossman, Weimann; TALG'10); however, Mao (FOCS'21) broke the cubic barrier for the tree edit distance problem using fast matrix multiplication. Given a parameter k as an upper bound on the distance, an O(n+k^2)-time algorithm for edit distance has been known since the 1980s due to the works of Myers (Algorithmica'86) and Landau and Vishkin (JCSS'88). The existence of an Õ(n+poly(k))-time algorithm for tree edit distance has been posed as an open question, e.g., by Akmal and Jin (ICALP'21), who gave a state-of-the-art Õ(nk^2)-time algorithm. In this paper, we answer this question positively.Debarati Das, Jacob Gilbert, MohammadTaghi Hajiaghayi, Tomasz Kociumaka, Barna Saha, Hamed Salehwork_pw2nujktqzf7ldxjjrw2uu4jt4Thu, 15 Sep 2022 00:00:00 GMTAlgorithms and Lower Bounds for Replacement Paths under Multiple Edge Failures
https://scholar.archive.org/work/jg4hvy33cjbn7n2tynpjd46fr4
This paper considers a natural fault-tolerant shortest paths problem: for some constant integer f, given a directed weighted graph with no negative cycles and two fixed vertices s and t, compute (either explicitly or implicitly) for every tuple of f edges, the distance from s to t if these edges fail. We call this problem f-Fault Replacement Paths (fFRP). We first present an Õ(n^3) time algorithm for 2FRP in n-vertex directed graphs with arbitrary edge weights and no negative cycles. As 2FRP is a generalization of the well-studied Replacement Paths problem (RP) that asks for the distances between s and t for any single edge failure, 2FRP is at least as hard as RP. Since RP in graphs with arbitrary weights is equivalent in a fine-grained sense to All-Pairs Shortest Paths (APSP) [Vassilevska Williams and Williams FOCS'10, J. ACM'18], 2FRP is at least as hard as APSP, and thus a substantially subcubic time algorithm in the number of vertices for 2FRP would be a breakthrough. Therefore, our algorithm in Õ(n^3) time is conditionally nearly optimal. Our algorithm implies an Õ(n^f+1) time algorithm for the fFRP problem, giving the first improvement over the straightforward O(n^f+2) time algorithm. Then we focus on the restriction of 2FRP to graphs with small integer weights bounded by M in absolute values. Using fast rectangular matrix multiplication, we obtain a randomized algorithm that runs in Õ(M^2/3n^2.9153) time. This implies an improvement over our Õ(n^f+1) time arbitrary weight algorithm for all f>1. We also present a data structure variant of the algorithm that can trade off pre-processing and query time. In addition to the algebraic algorithms, we also give an n^8/3-o(1) conditional lower bound for combinatorial 2FRP algorithms in directed unweighted graphs.Virginia Vassilevska Williams, Eyob Woldeghebriel, Yinzhan Xuwork_jg4hvy33cjbn7n2tynpjd46fr4Thu, 15 Sep 2022 00:00:00 GMTRelative Survivable Network Design
https://scholar.archive.org/work/5kilgevqwrggrfvqm5anarzw2a
One of the most important and well-studied settings for network design is edge-connectivity requirements. This encompasses uniform demands such as the Minimum k-Edge-Connected Spanning Subgraph problem (k-ECSS), as well as nonuniform demands such as the Survivable Network Design problem. A weakness of these formulations, though, is that we are not able to ask for fault-tolerance larger than the connectivity. Taking inspiration from recent definitions and progress in graph spanners, we introduce and study new variants of these problems under a notion of relative fault-tolerance. Informally, we require not that two nodes are connected if there are a bounded number of faults (as in the classical setting), but that two nodes are connected if there are a bounded number of faults and the two nodes are connected in the underlying graph post-faults. That is, the subgraph we build must "behave" identically to the underlying graph with respect to connectivity after bounded faults. We define and introduce these problems, and provide the first approximation algorithms: a (1+4/k)-approximation for the unweighted relative version of k-ECSS, a 2-approximation for the weighted relative version of k-ECSS, and a 27/4-approximation for the special case of Relative Survivable Network Design with only a single demand with a connectivity requirement of 3. To obtain these results, we introduce a number of technical ideas that may of independent interest. First, we give a generalization of Jain's iterative rounding analysis that works even when the cut-requirement function is not weakly supermodular, but instead satisfies a weaker definition we introduce and term local weak supermodularity. Second, we prove a structure theorem and design an approximation algorithm utilizing a new decomposition based on important separators, which are structures commonly used in fixed-parameter algorithms that have not commonly been used in approximation algorithms.Michael Dinitz, Ama Koranteng, Guy Kortsarz, Amit Chakrabarti, Chaitanya Swamywork_5kilgevqwrggrfvqm5anarzw2aThu, 15 Sep 2022 00:00:00 GMTVoting-based Opinion Maximization
https://scholar.archive.org/work/nl7fapklt5bq3ay4ief5x4ukvq
We investigate the novel problem of voting-based opinion maximization in a social network: Find a given number of seed nodes for a target campaigner, in the presence of other competing campaigns, so as to maximize a voting-based score for the target campaigner at a given time horizon. The bulk of the influence maximization literature assumes that social network users can switch between only two discrete states, inactive and active, and the choice to switch is frozen upon one-time activation. In reality, even when having a preferred opinion, a user may not completely despise the other opinions, and the preference level may vary over time due to social influence. To this end, we employ models rooted in opinion formation and diffusion, and use several voting-based scores to determine a user's vote for each of the multiple campaigners at a given time horizon. Our problem is NP-hard and non-submodular for various scores. We design greedy seed selection algorithms with quality guarantees for our scoring functions via sandwich approximation. To improve the efficiency, we develop random walk and sketch-based opinion computation, with quality guarantees. Empirical results validate our effectiveness, efficiency, and scalability.Arkaprava Saha, Xiangyu Ke, Arijit Khan, Laks V.S. Lakshmananwork_nl7fapklt5bq3ay4ief5x4ukvqWed, 14 Sep 2022 00:00:00 GMTReaching a Consensus with Limited Information
https://scholar.archive.org/work/izscgizkdnhxhhlkqj567wk2yy
In its simplest form the well known consensus problem for a networked family of autonomous agents is to devise a set of protocols or update rules, one for each agent, which can enable all of the agents to adjust or tune their "agreement variable" to the same value by utilizing real-time information obtained from their "neighbors" within the network. The aim of this paper is to study the problem of achieving a consensus in the face of limited information transfer between agents. By this it is meant that instead of each agent receiving an agreement variable or real-valued state vector from each of its neighbors, it receives a linear function of each state instead. The specific problem of interest is formulated and provably correct algorithms are developed for a number of special cases of the problem.Jingxuan Zhu, Yixuan Lin, Ji Liu, A. Stephen Morsework_izscgizkdnhxhhlkqj567wk2yyWed, 14 Sep 2022 00:00:00 GMTRevealing the similarity between urban transportation networks and optimal transport-based infrastructures
https://scholar.archive.org/work/ca4na5wxg5h7hj23evib6er23m
Designing and optimizing the structure of urban transportation networks is a challenging task. In this study, we propose a method inspired by optimal transport theory to reproduce the optimal structure of public transportation networks, that uses little information in input. Contrarily to standard approaches, it does not assume any initial backbone network infrastructure, but rather extracts this directly from a continuous space using only a few origin and destination points. Analyzing a set of urban rail, tram and subway networks, we find a high degree of similarity between simulated and real infrastructures. By tuning one parameter, our method can simulate a range of different networks that can be further used to suggest possible improvements in terms of relevant transportation properties. Outputs of our algorithm provide naturally a principled quantitative measure of similarity between two networks that can be used to automatize the selection of similar simulated networks.Daniela Leite, Caterina De Baccowork_ca4na5wxg5h7hj23evib6er23mWed, 14 Sep 2022 00:00:00 GMTNonlinear Spectral Duality
https://scholar.archive.org/work/yvgocaetzbddbphoh2w75zt2cm
Nonlinear eigenvalue problems for pairs of homogeneous convex functions are particular nonlinear constrained optimization problems that arise in a variety of settings, including graph mining, machine learning, and network science. By considering different notions of duality transforms from both classical and recent convex geometry theory, in this work we show that one can move from the primal to the dual nonlinear eigenvalue formulation maintaining the spectrum, the variational spectrum as well as the corresponding multiplicities unchanged. These nonlinear spectral duality properties can be used to transform the original optimization problem into various alternative and possibly more treatable dual problems. We illustrate the use of nonlinear spectral duality in a variety of example settings involving optimization problems on graphs, nonlinear Laplacians, and distances between convex bodies.Francesco Tudisco, Dong Zhangwork_yvgocaetzbddbphoh2w75zt2cmTue, 13 Sep 2022 00:00:00 GMT