On canonical representation of convex polyhedra

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An alcoved polytope is a polytope whose facet normal are all in direction of type A roots. This fundamental class of polytopes has ample applications in for instance tropical geometry, statistics and algebra. The mini-workshop showcased various recent deve ...

Testing graph cluster structure has been a central object of study in property testing since the foundational work of Goldreich and Ron [STOC’96] on expansion testing, i.e. the problem of distinguishing between a single cluster (an expander) and a graph th ...

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...

Spectral algorithms are some of the main tools in optimization and inference problems on graphs. Typically, the graph is encoded as a matrix and eigenvectors and eigenvalues of the matrix are then used to solve the given graph problem. Spectral algorithms ...

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We examine the connection of two graph parameters, the size of a minimum feedback arcs set and the acyclic disconnection. A feedback arc set of a directed graph is a subset of arcs such that after deletion the graph becomes acyclic. The acyclic disconnecti ...

Orthogonal group synchronization is the problem of estimating n elements Z(1),& mldr;,Z(n) from the rxr orthogonal group given some relative measurements R-ij approximate to Z(i)Z(j)(-1). The least-squares formulation is nonconvex. To avoid its local minim ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional opti ...

Let K be an algebraically closed field of characteristic zero, and let G be a connected reductive algebraic group over K. We address the problem of classifying triples (G, H, V ), where H is a proper connected subgroup of G, and V is a finitedimensional ir ...

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This article focuses on spectral methods for recovering communities in temporal networks. In the case of fixed communities, spectral clustering on the simple time-aggregated graph (i.e., the weighted graph formed by the sum of the interactions over all tem ...

We develop a very general version of the hyperbola method which extends the known method by Blomer and Brudern for products of projective spaces to complete smooth split toric varieties. We use it to count Campana points of bounded log-anticanonical height ...

Technology mapping transforms a technology-independent representation into a technology-dependent one given a library of cells. This process is performed by means of local replacements that are extracted by matching sections of the subject graph to library ...

We prove that the Cohn-Elkies linear programming bound for sphere packing is not sharp in dimension 6. The proof uses duality and optimization over a space of modular forms, generalizing a construction of Cohn- Triantafillou [Math. Comp. 91 (2021), pp. 491 ...

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as m ...

An integer linear program is a problem of the form max{c^T x : Ax=b, x >= 0, x integer}, where A is in Z^(n x m), b in Z^m, and c in Z^n. Solving an integer linear program is NP-hard in general, but there are several assumptions for which it becomes fixed ...

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

This paper considers the problem of second-degree price discrimination when the type distribution is unknown or imperfectly specified by means of an ambiguity set. As robustness measure we use a performance index, equivalent to relative regret, which quant ...

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...

On canonical representation of convex polyhedra

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Mini-Workshop: Alcoved Polytopes in Physics and Optimization

Approximating Dasgupta Cost in Sublinear Time from a Few Random Seeds

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

The Power of Two Matrices in Spectral Algorithms for Community Recovery

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

The connection of the acyclic disconnection and feedback arc sets - On an open problem of Figueroa et al

BENIGN LANDSCAPES OF LOW-DIMENSIONAL RELAXATIONS FOR ORTHOGONAL SYNCHRONIZATION ON GENERAL GRAPHS

Graph generative deep learning models with an application to circuit topologies

Learning to Remove Cuts in Integer Linear Programming

Multiplicity-free Representations of Algebraic Groups

A ride time-oriented scheduling algorithm for dial-a-ride problems

Recovering Static and Time-Varying Communities Using Persistent Edges

HYPERBOLA METHOD ON TORIC VARIETIES

In Medio Stat Virtus: Combining Boolean and Pattern Matching

Six-dimensional sphere packing and linear programming

Molecular hypergraph neural networks

Results on Sparse Integer Programming and Geometric Independent Sets

Equivariant Neural Architectures for Representing and Generating Graphs

Price Discrimination with Robust Beliefs

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables