Cover Image

Continuum-marginal optimal transport: a mesh-free kernel method

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Nakano, Yumiharu
Format:	Preprint
Published:	2026
Subjects:	Optimization and Control Numerical Analysis Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A kernel-based method for Schrödinger bridges
by: Nakano, Yumiharu
Published: (2023)

Kernel-based potential mean-field games with unbiased random Fourier $U$-statistics
by: Nakano, Yumiharu
Published: (2026)

Convergence of differentiable non-monotone schemes for fully nonlinear parabolic equations
by: Nakano, Yumiharu
Published: (2018)

Solving Monge problem by Hilbert space embeddings of probability measures
by: Saito, Takafumi, et al.
Published: (2024)

A distributed semismooth Newton based augmented Lagrangian method for distributed optimization
by: Ma, Qihao, et al.
Published: (2026)

First-order methods for stochastic and finite-sum convex optimization with deterministic constraints
by: Lu, Zhaosong, et al.
Published: (2025)

Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
by: Dereich, Steffen, et al.
Published: (2024)

Numerical method for feasible and approximately optimal solutions of multi-marginal optimal transport beyond discrete measures
by: Neufeld, Ariel, et al.
Published: (2022)

Nonlinear model reduction for transport-dominated problems
by: Hesthaven, Jan S., et al.
Published: (2026)

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems
by: Dereich, Steffen, et al.
Published: (2025)

A deep learning approach to multi-marginal optimal transport via Hilbert space embeddings of probability measures
by: Nakano, Yumiharu, et al.
Published: (2025)

Cubic regularized subspace Newton for non-convex optimization
by: Zhao, Jim, et al.
Published: (2024)

Particle method for a nonlinear multimarginal optimal transport problem
by: Cances, Adrien, et al.
Published: (2026)

Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance
by: Stollenwerk, Alexander, et al.
Published: (2024)

PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning
by: Jentzen, Arnulf, et al.
Published: (2025)

Neural incomplete factorization: learning preconditioners for the conjugate gradient method
by: Häusner, Paul, et al.
Published: (2023)

Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models
by: Tomasetto, Matteo, et al.
Published: (2024)

RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees
by: Laufer, Eilon Vaknin, et al.
Published: (2025)

PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
by: Goujaud, Baptiste, et al.
Published: (2022)

A single-loop SPIDER-type stochastic subgradient method for expectation-constrained nonconvex nonsmooth optimization
by: Liu, Wei, et al.
Published: (2025)

Numerical method for approximately optimal solutions of two-stage distributionally robust optimization with marginal constraints
by: Neufeld, Ariel, et al.
Published: (2022)

Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient
by: Lim, Dong-Young, et al.
Published: (2022)

A trust-region framework for optimization using Hermite kernel surrogate models
by: Ullmann, Sven, et al.
Published: (2025)

On the numerical reliability of nonsmooth autodiff: a MaxPool case study
by: Boustany, Ryan
Published: (2024)

Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
by: Tyurin, Alexander
Published: (2025)

End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
by: Ma, Shaocong, et al.
Published: (2024)

Suboptimality bounds for trace-bounded SDPs enable a faster and scalable low-rank SDP solver SDPLR+
by: Huang, Yufan, et al.
Published: (2024)

Gradient is All You Need? How Consensus-Based Optimization can be Interpreted as a Stochastic Relaxation of Gradient Descent
by: Riedl, Konstantin, et al.
Published: (2023)

Derivative-free optimization is competitive for aerodynamic design optimization in moderate dimensions
by: Plaban, Punya, et al.
Published: (2025)

Unidimensional semi-discrete partial optimal transport
by: Cances, Adrien, et al.
Published: (2025)

Creating walls to avoid unwanted points in root finding and optimization
by: Truong, Tuyen Trung
Published: (2023)

Dynamic Proximal Gradient Algorithms for Schatten-$p$ Quasi-Norm Regularized Problems
by: Shen, Weiping, et al.
Published: (2026)

Why is Normalization Preferred? A Worst-Case Complexity Theory for Stochastically Preconditioned SGD under Heavy-Tailed Noise
by: Fang, Yuchen, et al.
Published: (2026)

Natural Riemannian gradient for learning functional tensor networks
by: Klug, Nikolas, et al.
Published: (2026)

State-Dependent Lyapunov Method for Rank-1 Matrix Factorization
by: Moon, Jaehong
Published: (2026)

Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints
by: Zhu, Linglingzhi, et al.
Published: (2026)

Solving the Offline and Online Min-Max Problem of Non-smooth Submodular-Concave Functions: A Zeroth-Order Approach
by: Farzin, Amir Ali, et al.
Published: (2026)

WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points
by: Li, Dongyue, et al.
Published: (2026)

Last-Iterate Convergence of Randomized Kaczmarz and SGD with Greedy Step Size
by: Dereziński, Michał, et al.
Published: (2026)

OptEMA: Adaptive Exponential Moving Average for Stochastic Optimization with Zero-Noise Optimality
by: Yuan, Ganzhao
Published: (2026)