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.
Exact discrete mechanics for nonholonomic systems defined.
The paper explores how to learn models that respect constraints in probabilistic learning.
This paper studies Hamilton-Jacobi equations for magnetic systems with constraints.
Optimal transport with path constraints for distributions of different masses.
Optimizes insurance profits under regulatory constraints.
We discuss the explicit formulation of the transcendental constraints defining spectral curves of SU(2) BPS monopoles in the twistor approach of Hitchin, following Ercolani and Sinha. We obtain an improved version of the Ercolani-Sinha constraints, and show that the Corrigan-Goddard conditions for constructing monopole…
New metrics improve landing algorithms for orthogonality constraints.
We construct solutions with prescribed asymptotics to the Einstein constraint equations using a cut-off technique. Moreover, we give various examples of vacuum asymptotically flat manifolds whose center of mass and angular momentum are ill-defined.
The paper defines constraints for commuting endomorphisms in generalized tangent bundles.
Geometrically characterizes virtual nonlinear nonholonomic constraints using symplectic methods.
The paper defines and proves equivalence of nonholonomic brackets in contact mechanical systems.
Improved text generation with constraints using discrete auto-regressive biasing.
This paper presents a geometric description of Lagrangian and Hamiltonian systems on Lie affgebroids subject to affine nonholonomic constraints. We define the notion of nonholonomically constrained system, and characterize regularity conditions that guarantee that the dynamics of the system can be obtained as a suitabl…
The extended constraint equations arise as a special case of the conformal constraint equations that are satisfied by an initial data hypersurface in an asymptotically simple spacetime satisfying the vacuum conformal Einstein equations developed by H. Friedrich. The extended constraint equations consist of a quasi-…
This paper introduces Sigma, a domain-specific computational representation for collaboration in large-scale for the field of economics. A computational representation is not a programming language or a software platform. A computational representation is a domain-specific representation system based on three specific …
Simplifies neural network constraints with computationally efficient method.
We propose a general method for deformation quantization of any second-class constrained system on a symplectic manifold. The constraints determining an arbitrary constraint surface are in general defined only locally and can be components of a section of a non-trivial vector bundle over the phase-space manifold. The c…
Study introduces constrained neural units over time for learning problems.
Formula for Laplace-Beltrami on orthogonal group in Euclidean coords.
New boundary and point constraints for controlling conformal surfaces.
Study optimality conditions for interval-valued optimization problems on Riemannian manifolds.
This work removes logarithmic singularities from hyperboloidal initial data without creating new ones.
A new geometrical setting for classical field theories is introduced. This description is strongly inspired in the one due to Skinner and Rusk for singular lagrangians systems. For a singular field theory a constraint algorithm is developed that gives a final constraint submanifold where a well-defined dynamics exists.…
Consistent algorithms for multiclass learning with complex metrics and constraints.
The constraints arising from DAG models with latent variables can be naturally represented by means of acyclic directed mixed graphs (ADMGs). Such graphs contain directed and bidirected arrows, and contain no directed cycles. DAGs with latent variables imply independence constraints in the distribution resulting from a…
Despite their popularity, many questions about the algebraic constraints imposed by linear structural equation models remain open problems. For causal discovery, two of these problems are especially important: the enumeration of the constraints imposed by a model, and deciding whether two graphs define the same statist…
Defines constraint tensor for null hypersurfaces, providing explicit geometry.
New method for sampling on constrained domains using orthogonal-space gradient flow.
The modified Korteweg-de Vries hierarchy (mKdV) is derived by imposing isometry and isoenergy conditions on a moduli space of plane loops. The conditions are compared to the constraints that define Euler's elastica. Moreover, the conditions are shown to be constraints on the curvature and other invariants of the loops …
New diffusion models handle constrained domains, improving generative tasks.
Stochastic kernel based dimensionality reduction approaches have become popular in the last decade. The central component of many of these methods is a symmetric kernel that quantifies the vicinity between pairs of data points and a kernel-induced Markov chain on the data. Typically, the Markov chain is fully specified…
An algorithm solves optimization problems with large sample sets, improving worst-case complexity.
Framework for controlling multiple risks in AI models.
In classification models fairness can be ensured by solving a constrained optimization problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and Equalized Odds, which are non-decomposable and non-convex. Researchers define convex surrogates of the constraints and then apply convex optimiz…
OLLA framework efficiently samples from constrained distributions with nonconvex constraints.
Although conservative Hamiltonian systems with constraints can be formulated in terms of Dirac structures, a more general framework is necessary to cover also dissipative systems such as gradient and metriplectic systems with constraints. We define Leibniz-Dirac structures which lead to a natural generalization of Dira…
We discuss the portfolio optimization problem with the obligatory deposits constraint. Recently it has been shown that as a consequence of this nonlinear constraint, the solution consists of an exponentially large number of optimal portfolios, completely different from each other, and extremely sensitive to any changes…
Study a continuous portfolio optimization with a new CVaR-like constraint using martingale approach.
We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type "objective A is more important than objective B". These preferences are defined based on the stability of the obtained solutions with respect to preferred objective fun…
Study binary hypothesis testing with privacy and communication constraints.
Optimizes multi-period portfolios with tail-risk constraints using neural networks.
Defines Killing spinors and bosonic backgrounds in 5D supergravity.
New method optimizes processes under constraints using bivariate Gaussian models.
In this paper, we propose a geometric integrator for nonholonomic mechanical systems. It can be applied to discrete Lagrangian systems specified through a discrete Lagrangian defined on QxQ, where Q is the configuration manifold, and a (generally nonintegrable) distribution in TQ. In the proposed method, a discretizati…
Optimization results are one method for understanding neural computation from Nature's perspective and for defining the physical limits on neuron-like engineering. Earlier work looks at individual properties or performance criteria and occasionally a combination of two, such as energy and information. Here we make use …
Investment strategy for DC pension plan with inflation risk and tail VaR constraint.
Study examines how business units can benefit from group cohesion under regulatory constraints.
Bayesian Entropy Neural Networks enforce constraints on deep learning predictions.