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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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103206308411 · Jun 202019922001200920182026
48 results for combinatorial graph reductions

Method calculates polytope volume using graph combinatorics and Kirillov-Reshetikhin invariants.

problem Computing the volume of hyperbolic polyhedra.
method Combining combinatorial graph reductions and geometric splitting into tetrahedra.
result Volume of polytope can be expressed through critical values of a potential function.

A new method reduces complexity and uncertainty in neural networks.

problem Uncertainty quantification in complex neural networks.
method Condensed Stein Variational Gradient Descent (cSVGD) method.
result Condensed SVGD provides uncertainty quantification on parameters.

We describe a new variational lower-bound on the minimum energy configuration of a planar binary Markov Random Field (MRF). Our method is based on adding auxiliary nodes to every face of a planar embedding of the graph in order to capture the effect of unary potentials. A ground state of the resulting approximation can…

2011-04-06abs ↗pdf ↗

COMBO optimizes Bayesian Optimization for combinatorial search spaces.

problem Optimizing objectives on combinatorial search spaces with high-order interactions.
method COMBO uses a combinatorial graph and ARD diffusion kernel with Horseshoe prior for efficient modeling and variable selection.
result COMBO outperforms state-of-the-art methods consistently across various benchmarks.

Paper develops a novel approach for unsupervised dimension selection.

problem Tackles the combinatorial problem of identifying top-k dimensions in high-dimensional data.
method Develops a novel approach based on graph signal analysis to measure feature influence.
result Demonstrates the superiority of the proposed approach over existing techniques in capturing crucial characteristics of high-dimensional spaces using only a small subset of features.

The paper finds minimum Steklov eigenvalues on combinatorial graphs.

problem Finding the minimum Steklov eigenvalues on combinatorial graphs.
method Extending Friedman's nodal domain theory for Laplacian eigenfunctions to Steklov eigenfunctions.
result The minimum of the imthi^{ m th} Steklov eigenvalue on a connected combinatorial graph is essentially attained by a star or a regular comb with minimal brooms.

Surveying machine learning for solving graph optimization problems.

problem Solving combinatorial optimization problems on graphs requires algorithmic engineering.
method Surveying machine learning approaches for graph optimization.
result Machine learning offers new ways to solve graph optimization problems.

Graph neural networks improve combinatorial optimization by leveraging inductive bias.

problem Combinatorial optimization problems often arise from related data distributions.
method Using graph neural networks to enhance or solve combinatorial tasks.
result Graph neural networks effectively encode combinatorial and relational input.

The article introduces a combinatorial differential algebra for cubic planar graphs.

problem Understanding the structure of cubic planar graphs.
method Defining a combinatorial differential graded algebra based on binary sequences and counting.
result The algebra's graded augmentation variety's rational points match (q+1)-colorings of the dual graph.

We present a simple combinatorial model for quasipositive surfaces and positive braids, based on embedded bipartite graphs. As a first application, we extend the well-known duality on standard diagrams of torus links to twisted torus links. We then introduce a combinatorial notion of adjacency for bipartite graph links…

2011-11-16abs ↗pdf ↗

Extends knot concordance invariant to balanced spatial graphs using grid homology.

problem Defining a concordance invariant for balanced spatial graphs.
method Using grid homology to extend the invariant from knots to spatial graphs.
result The combinatorial ΥΥ invariant is a concordance invariant for balanced spatial graphs.

Reduces conjecture for Artin groups to simpler cases.

problem Proving K(π,1)K(π,1) for Artin groups with specific spherical parabolics.
method Reduces to simpler cases, uses injective metric spaces, combinatorial convexity, and Bestvina-type inequalities.
result Deduces K(π,1)K(π,1) conjecture for specific Artin groups.

This work proposes an unsupervised neural network framework for solving combinatorial optimization problems on graphs.

problem Challenges in neural networks solving combinatorial optimization problems without labeled instances.
method Inspired by Erdos' probabilistic method, a neural network parametrizes a probability distribution over sets, optimizing it to find low-cost integral solutions.
result The method provides valid solutions to the maximum clique problem and local graph clustering, achieving competitive results.

Finite subdivision rules in high dimensions are shown to be equivalent to 3D rules.

problem Visualization and construction of high-dimensional subdivision rules.
method Characterized history graphs and defined combinatorial subdivision rules.
result Finite subdivision rules in arbitrary dimensions are combinatorially equivalent to 3D rules.

This paper extends combinatorial semi-bandits to graph feedback, improving regret bounds.

problem Adversarial combinatorial semi-bandits with graph feedback.
method Introduced graph feedback in combinatorial semi-bandits, using convexified actions and online stochastic mirror descent.
result Optimal regret scales as ST+αSTS\sqrt{T}+\sqrt{αST}, interpolating between full and semi-bandit feedback.

RNNs learn combinatorial graph problems with sample complexity bounds.

problem Learning efficient approximations for real-valued combinatorial graph problems.
method Upper bounds the sample complexity for learning real-valued RNNs.
result Real-valued RNNs can be learned with polynomial number of samples.

GCOMB learns heuristics for large graphs efficiently.

problem Scalability and practical constraints in graph problem solving.
method GCOMB uses a Graph Convolutional Network (GCN) with Q-learning for efficient heuristic discovery.
result GCOMB is 100 times faster and marginally better than state-of-the-art algorithms.

Novel technique reduces Bayesian network complexity while preserving inference accuracy.

problem Complexity reduction in Bayesian networks for efficient inference.
method Directed convex hull structure and polynomial-time algorithm for identifying minimum localized networks.
result High dimension reduction capability and improved inference efficiency in real networks.

For a graph embedded into a surface, we relate many combinatorial parameters of the cycle matroid of the graph and the bond matroid of the dual graph with the topological parameters of the embedding. This will give an expression of the polynomial, defined by M.Las Vergnas in a combinatorial way using matroids as a spec…

2010-12-22abs ↗pdf ↗

This paper tackles combinatorial pure exploration for dueling bandits, aiming to find the best candidate-position match.

problem Finding the best candidate-position match in a dueling bandit setting.
method The paper adapts combinatorial pure exploration for multi-armed bandits to dueling bandits, considering both Borda winner and Condorcet winner cases. It designs PAC and exact algorithms for Borda winner and a fully polynomial time approximation scheme (FPTAS) for Condorcet winner.
result The paper introduces the first algorithm with polynomial running time per round for identifying the Condorcet winner in CPE-DB.

Neural network and RL solve combinatorial problems like TSP and KnapSack.

problem Combinatorial optimization problems like TSP and KnapSack.
method Recurrent neural network trained with policy gradient on city permutations and item sets.
result Achieves close to optimal solutions for up to 100 nodes in TSP and 200 items in KnapSack.

New algorithm reduces regret in combinatorial causal bandits without graph structure.

problem Minimizing regret in combinatorial causal bandits without graph structure.
method Design of algorithms for binary general causal models and BGLMs without graph skeleton.
result Achieves O(TlnT)O(\sqrt{T}\ln T) expected regret for causal models and O(T23lnT)O(T^{\frac{2}{3}}\ln T) for BGLMs.

Automates learning combinatorial optimization algorithms over graphs.

problem Designing good heuristics for NP-hard problems requires specialized knowledge.
method Combines reinforcement learning and graph embedding.
result Learned algorithms effectively solve Minimum Vertex Cover, Maximum Cut, and Traveling Salesman problems.

A planar graph is inscribable if it is combinatorial equivalent to the skeleton of a polyhedra which is inscribed in a sphere. For an inscribable graph, in its combinatorial equivalent class, if we could always find polyhedra inscribed in any given convex surface which is sufficiently close to the sphere, then we call …

2014-12-15abs ↗pdf ↗

The Kac-Ward formula allows to compute the Ising partition function on a planar graph G with straight edges from the determinant of a matrix of size 2N, where N denotes the number of edges of G. In this paper, we extend this formula to any finite graph: the partition function can be written as an alternating sum of the…

2010-04-19abs ↗pdf ↗

A method learns to solve multilevel combinatorial problems with two players.

problem Multilevel combinatorial optimization problems with multiple players.
method Value-based multi-agent reinforcement learning in a graph neural network framework.
result Close to optimal solutions on graphs up to 100 nodes, with a significant speedup.

Graph Pointer Networks and hierarchical reinforcement learning solve combinatorial optimization problems like TSP.

problem Traveling Salesman Problem (TSP) with constraints.
method Graph Pointer Networks (GPNs) and hierarchical reinforcement learning.
result GPNs and hierarchical RL find optimal solutions for TSP and TSP with time windows.

Combinatorial approach to compute satellite knot invariants using graph theory.

problem Computing knot invariants for satellite knots using bordered Heegaard Floer homology.
method Construct weighted AA_\infty-modules using decorated planar graphs and prove their isomorphism.
result Combinatorial proof of AA_\infty structure relations for the constructed modules.