#convergence analysis
9 resultsDesign and analysis of finite volume methods for elliptic equations with oblique derivatives; application to Earth gravity field modelling
Jerome Droniou, Matej Medla, Karol Mikula
The paper proposes and analyses finite volume schemes for Poisson problems with oblique derivative boundary conditions, providing a generic framework, convergence proofs, and 3D te…
An inexact strategy for the projected gradient algorithm in vector optimization problems on variable ordered spaces
Jose Yunier Bello Cruz, Gemayqzel Bouza Allende
The paper proposes an inexact projected gradient algorithm for smooth constrained vector optimization problems defined on variable ordered spaces, proves convergence to weakly effi…
Continuum limit of discrete Sommerfeld problems on square lattice
Basant Lal Sharma
The paper analyzes low‑frequency discrete Sommerfeld diffraction problems on a square lattice and proves that the discrete solutions for Dirichlet and Neumann half‑planes converge…
On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond
Xiao-Tong Yuan, Ping Li
The paper introduces new variants of the DANE distributed approximate Newton algorithm, adding backtracking line search and a heavy‑ball acceleration to achieve global convergence…
Conditional quantile sequential estimation for stochastic codes
Tatiana Labopin-Richard, Fabrice Gamboa, Aurélien Garivier +1
The paper introduces a sequential algorithm that estimates conditional quantiles for stochastic computer codes with vector inputs, using k‑nearest neighbor smoothing inside a Robbi…
A novel linearized and momentum-preserving Fourier pseudo-spectral scheme for the Rosenau-Korteweg de Vries equation
Chaolong Jiang, Jin Cui, Wenjun Cai +1
The paper proposes a new linearized Fourier pseudo-spectral method that preserves momentum for solving the Rosenau‑Korteweg‑de Vries equation and proves its convergence without mes…
BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization
Il Yong Chun, Xuehang Zheng, Yong Long +1
The paper presents a modified BCD-Net architecture for low‑dose CT reconstruction that speeds up the iterative process, provides convergence guarantees, and generalizes better to c…
On the modes of convergence of Stochastic Optimistic Mirror Descent (OMD) for saddle point problems
Yanting Ma, Shuchin Aeron, Hassan Mansour
The paper analyzes the convergence behavior of Mirror Descent and Optimistic Mirror Descent algorithms on coherent saddle‑point problems, correcting earlier claims and establishing…
SVGD: A Virtual Gradients Descent Method for Stochastic Optimization
Zheng Li, Shi Shu
The paper introduces Stochastic Virtual Gradient Descent (SVGD), a memory‑efficient stochastic optimization algorithm that defines gradients via computational graphs and automatic…