FDR achieves the optimal O(1/N^2) rate with improved constants for proximal minimization of convex plus strongly convex functions and proves a matching lower bound.
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A first-order primal-dual algorithm for convex prob- lems with applications to imaging.J
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Direct fixed-weight solver for free-support Wasserstein medians relocates atoms using OT barycentric projections and inverse-distance weights, achieving monotone descent on smoothed objectives with fewer subproblems than nested Weiszfeld baselines.
Uncorrected Gaussian residual penalties in full-space sampling converge after marginalization to the graph-lifted reduced posterior multiplied by the inverse absolute determinant of the state Jacobian, requiring explicit determinant corrections for equivalence.
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
Matrix-valued optimism equals matrix-valued augmentation additively for symmetric parameters, enabling closed-form hybrid designs that improve finite-step feasibility in constrained optimization.
ML-SPnP accelerates stochastic PnP for SVCT by using MRA approximation spaces where prior-coherence corrections vanish in expectation, yielding comparable quality at reduced runtime.
Presents a coupled prediction-correction variant of Hughes' crowd model and uses numerical simulations to suggest progress toward its well-posedness.
Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.
VSLP infers dense segmentations from global label proportions via a pre-trained transformer for initial confidence maps followed by variational optimization using Wasserstein fidelity and a learned regularizer, outperforming prior weakly supervised methods on histopathology datasets.
SiPO decouples projection shape from dose scaling in TVAM by turning normalized conformity and spillage metrics into a solvable linear program with two practical control cases.
A review reframing density estimation as 'density evolution' across scales, linking kernel smoothing to heat flow, mixtures to compression, and topology to level sets, while stating three structural results on modes, Gaussian semigroups, and log-concavity.
Authors create psychometrically scaled image sets from human tests on denoised photos and provide a HaarPSI threshold for choosing denoising parameters based on perceived similarity.
A DRL-trained unrolled QP network serves as a model-free safety filter with formal persistent safety guarantees.
lpviz is a web-based interactive visualization tool for linear programming that enables direct graphical editing of problems and comparison of multiple solver algorithms in 2D and 3D.
Proposes and empirically tests an adaptive double-phase ROF denoising model that reduces staircasing while preserving edges, reporting improved or similar SSIM, PSNR, and LPIPS performance.
Subgradient Langevin dynamics and certain discretizations are shown to be ergodic for strongly convex non-smooth potentials, with the discrete versions also satisfying the law of large numbers.
citing papers explorer
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Optimal Acceleration for Proximal Minimization of the Sum of Convex and Strongly Convex Functions
FDR achieves the optimal O(1/N^2) rate with improved constants for proximal minimization of convex plus strongly convex functions and proves a matching lower bound.
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Fast Computation of Free-Support Wasserstein Medians
Direct fixed-weight solver for free-support Wasserstein medians relocates atoms using OT barycentric projections and inverse-distance weights, achieving monotone descent on smoothed objectives with fewer subproblems than nested Weiszfeld baselines.
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Constraint residuals, graph posteriors, and determinant-corrected full-space targets in Bayesian inverse problems
Uncorrected Gaussian residual penalties in full-space sampling converge after marginalization to the graph-lifted reduced posterior multiplied by the inverse absolute determinant of the state Jacobian, requiring explicit determinant corrections for equivalence.
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Scale-Calibrated Median-of-Means for Robust Distributed Principal Component Analysis
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
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Matrix-Valued Optimism is Matrix-Valued Augmentation: Additive Hybrid Designs for Constrained Optimization
Matrix-valued optimism equals matrix-valued augmentation additively for symmetric parameters, enabling closed-form hybrid designs that improve finite-step feasibility in constrained optimization.
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Multilevel Stochastic Plug-and-Play for Sparse-View CT Reconstruction
ML-SPnP accelerates stochastic PnP for SVCT by using MRA approximation spaces where prior-coherence corrections vanish in expectation, yielding comparable quality at reduced runtime.
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A coupled prediction-correction Hughes' model for congested crowd motion
Presents a coupled prediction-correction variant of Hughes' crowd model and uses numerical simulations to suggest progress toward its well-posedness.
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Scale selection for geometric medians on product manifolds
Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.
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Semantic Segmentation for Histopathology using Learned Regularization based on Global Proportions
VSLP infers dense segmentations from global label proportions via a pre-trained transformer for initial confidence maps followed by variational optimization using Wasserstein fidelity and a learned regularizer, outperforming prior weakly supervised methods on histopathology datasets.
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Scale-invariant projection optimization in tomographic volumetric additive manufacturing
SiPO decouples projection shape from dose scaling in TVAM by turning normalized conformity and spillage metrics into a solvable linear program with two practical control cases.
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Density Evolution: A Multiscale View of Density Estimation
A review reframing density estimation as 'density evolution' across scales, linking kernel smoothing to heat flow, mixtures to compression, and topology to level sets, while stating three structural results on modes, Gaussian semigroups, and log-concavity.
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Mathematical framework for perception-driven parameter choice in image denoising
Authors create psychometrically scaled image sets from human tests on denoised photos and provide a HaarPSI threshold for choosing denoising parameters based on perceived similarity.
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Verifiable Model-Free Safety Filters via Reinforcement Learning
A DRL-trained unrolled QP network serves as a model-free safety filter with formal persistent safety guarantees.
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lpviz: Interactive Linear Programming Visualization
lpviz is a web-based interactive visualization tool for linear programming that enables direct graphical editing of problems and comparison of multiple solver algorithms in 2D and 3D.
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Adaptive double-phase Rudin--Osher--Fatemi denoising model
Proposes and empirically tests an adaptive double-phase ROF denoising model that reduces staircasing while preserving edges, reporting improved or similar SSIM, PSNR, and LPIPS performance.
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Ergodicity of Langevin Dynamics and its Discretizations for Non-smooth Potentials
Subgradient Langevin dynamics and certain discretizations are shown to be ergodic for strongly convex non-smooth potentials, with the discrete versions also satisfying the law of large numbers.
- Generalization of Zeroth-Order Method for Quotients of Quadratic Functions
- The Chambolle-Pock method converges weakly with $0 < \theta \le 1/2$ and $\tau\sigma\|L\|^{2} < 4\theta(2-\theta)/(1 - 2\theta + 9\theta^{2} - 4\theta^{3})$