arXiv research

Paper proves unique energy-minimizing curves in constrained spaces.

The paper classifies a special family of knots in lens spaces using knot Floer homology.

We show that the homogeneous and the 2-lobe Delaunay tori in the 3-sphere provide the only isothermic constrained Willmore tori in 3-space with Willmore energy below $8π$. In particular, every constrained Willmore torus with Willmore energy below $8π$ and non-rectangular conformal class is non-degenerated.

Develops G-MLKM for better data-target association in constrained spaces.

Study geodesics in constrained curve spaces, including elastic curves and concentric circles.

Constrained Willmore surfaces are conformal immersions of Riemann surfaces that are critical points of the Willmore energy $W=\int H^2$ under compactly supported infinitesimal conformal variations. Examples include all constant mean curvature surfaces in space forms. In this paper we investigate more generally the crit…

Algorithm optimizes constrained reinforcement learning with dual variables.

Constrained Willmore surfaces are critical points of the Willmore functional under conformal variations. As shown in [5] one can associate to any conformally immersed constrained Willmore torus f a compact Riemann surface Σ, such that f can be reconstructed in terms of algebraic data on Σ. Particularly interesting exam…

Study minimizers of quasi-perimeters in RCD spaces with volume constraints.

New algorithms for sampling in constrained domains without learning rates.

New model generates data on constrained sets without losing tractability.

Study compares constrained and decoupled moduli spaces of manifolds with particles and discs.

In reinforcement learning, an agent attempts to learn high-performing behaviors through interacting with the environment, such behaviors are often quantified in the form of a reward function. However some aspects of behavior-such as ones which are deemed unsafe and to be avoided-are best captured through constraints. W…

This work is dedicated to the study of the Moebius invariant class of constrained Willmore surfaces and its symmetries. We define a spectral deformation by the action of a loop of flat metric connections; Baecklund transformations, by applying a dressing action; and, in 4-space, Darboux transformations, based on the so…

Paper revisits DP-SCO in Euclidean and $\ell_p^d$ spaces, focusing on constrained and bounded sets.

This article reviews and explains HMC-based methods for sampling constrained continuous distributions.

New discrete curves defined in space forms with geometric properties.

New method for constrained sampling using gradient flows.

Study of star-shaped hypersurfaces with capillary boundary using constrained mean curvature flow.

New algorithm improves sampling from constrained spaces.

The paper proves geometric inequalities for pinched convex hypersurfaces in de Sitter space.

Study flow on de Sitter space for convex hypersurfaces.

Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA). Bayesian inference involving probability distributions confined to constrained d…

Proposes a constrained labeling method for weakly supervised learning.

The paper proves geometric inequalities in sphere using locally constrained flows.

In-BO optimizes complex constrained domains using SIn-GP surrogate models.

Method reformulates constrained optimization as latent space inference.

Let $S$ be a complete flat surface, such as the Euclidean plane. We obtain direct characterizations of the connected components of the space of all curves on $S$ which start and end at given points in given directions, and whose curvatures are constrained to lie in a given interval, in terms of all parameters involved.…

New algorithm samples constrained distributions efficiently.

Using results on the topology of moduli space of polygons [Jaggi, 92; Kapovich and Millson, 94], it can be shown that for a planar robot arm with $n$ segments there are some values of the base-length, $z$, at which the configuration space of the constrained arm (arm with its end effector fixed) has two disconnected com…

In this paper we consider two special classes of constrained Willmore tori in the 3-sphere. The first class is given by the rotation of closed elastic curves in the upper half plane - viewed as the hyperbolic plane - around the x-axis. The second is given as the preimage of closed constrained elastic curves, i.e., elas…

The class of non-rigid registration methods proposed in the framework of PDE-constrained Large Deformation Diffeomorphic Metric Mapping is a particularly interesting family of physically meaningful diffeomorphic registration methods. PDE-constrained LDDMM methods are formulated as constrained variational problems, wher…

We study configuration spaces of linkages whose underlying graph are polygons with diagonal constrains, or more general, partial two-trees. We show that (with an appropriate definition) the oriented area is a Bott-Morse function on the configuration space. Its critical points are described and Bott-Morse indices are co…

The paper solves a conjecture about spacelike hypersurfaces in de Sitter space.

A new framework uses an Incremental Transformer to design geopolymer mixtures efficiently.

We prove that the critical points of various energies such as the area, the Willmore energy, the frame energy for tori...etc among possibly branched immersions constrained to evolve within a smooth sub-manifold of the Teichmüller space satisfy the corresponding constrained Euler Lagrange equation. We deduce that critic…

CEI achieves convergence rates for constrained Bayesian optimization.

New flow for capillary surfaces converges to spherical caps.

Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space. We formulate a prior that incorporates functional constraints about what the output can or cannot be in regions of the input space. Output-Constrained BNNs (OC-BNN) represent an int…

Diffeologies unify infinite-dimensional geometry and PDEs, enhancing classical function spaces.

Shielded LMC samples from non-convex spaces with repulsive drift.

We propose a class of intrinsic Gaussian processes (in-GPs) for interpolation, regression and classification on manifolds with a primary focus on complex constrained domains or irregular shaped spaces arising as subsets or submanifolds of R, R2, R3 and beyond. For example, in-GPs can accommodate spatial domains arising…

Let $S$ be a complete flat surface, such as the Euclidean plane. We determine the homeomorphism class of the space of all curves on $S$ which start and end at given points in given directions and whose curvatures are constrained to lie in a given open interval, in terms of all parameters involved. Any connected compone…

PAC-MOO optimizes constrained multi-objective problems with preferences.

We improve Riemannian metrics for constrained systems control.

Proposes Constrained Q-learning for reinforcement learning with constraints.

Let $(M,g)$ be a 3-dimensional Riemannian manifold. The goal of the paper it to show that if $P_{0}\in M$ is a non-degenerate critical point of the scalar curvature, then a neighborhood of $P_{0}$ is foliated by area-constrained Willmore spheres. Such a foliation is unique among foliations by area-constrained Willmore …

Automatic Chemical Design is a framework for generating novel molecules with optimized properties. The original scheme, featuring Bayesian optimization over the latent space of a variational autoencoder, suffers from the pathology that it tends to produce invalid molecular structures. First, we demonstrate empirically …