New algorithm solves maximal monotone inclusion problems.
problem Solving maximal monotone inclusion problems.
method Bregman Douglas-Rachford splitting method and variants.
result Convergence of algorithms under certain assumptions.
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48 results for “monotone inclusion”
New algorithms solve monotone inclusions and convex-concave minimax problems.
problem Solving maximally monotone equations and inclusions.
method Developed new accelerated algorithms based on Halpern-type fixed-point iteration and Popov's past extra-gradient method.
result Achieved convergence rates for various problems.
Survey on extragradient methods for solving nonlinear equations and inclusions.
problem Approximating solutions of nonlinear equations and inclusions.
method Unified convergence analysis of extragradient and its variants.
result Sublinear convergence rates for different classes of algorithms.
New methods solve MI problems with locally Lipschitz operators, improving solution efficiency.
problem Solving monotone inclusions with locally Lipschitz continuous operators.
method Primal-dual extrapolation methods using backtracking line search.
result Improved operation complexity for solving MI problems.
Holonomy groups of metric connections converge in a monotonic way.
problem Monotonicity of holonomy groups under convergence of metric connections.
method Proving the monotonicity of holonomy groups for sequences of metric connections converging in .
result The holonomy group of the limit connection is contained in the holonomy group of the initial connections.
Revisiting Extragradient-Type Methods -- Part 1: Generalizations and Sublinear Convergence Ratesmath.OC
Paper generalizes extragradient methods for solving equations and inclusions with improved convergence rates.
problem Solving equations and inclusions using extragradient methods.
method Unified and generalized extragradient methods for a broader class of algorithms, analyzing sublinear convergence rates.
result Unified and improved convergence results for various extragradient variants.
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operatorsstat.ML
It is common to encounter large-scale monotone inclusion problems where the objective has a finite sum structure. We develop a general framework for variance-reduced forward-backward splitting algorithms for this problem. This framework includes a number of existing deterministic and variance-reduced algorithms for fun…
New algorithms reduce variance in solving complex mathematical problems.
problem Solving convex-concave saddle point problems, variational inequalities, and inclusions.
method Stochastic variance reduction for extragradient, forward-backward-forward, and forward-reflected-backward methods.
result All proposed methods converge with complexities matching or improving deterministic counterparts.
This paper advances extragradient methods for solving inclusions under co-hypomonotonicity.
problem Solving inclusions with non-Lipschitz mappings.
method Unified and generalized extragradient methods under co-hypomonotonicity.
result Achieved convergence rates for residual norms.
A new approach for instance-optimal learning that bypasses impossibility results.
problem Impossibility of achieving marginal-by-marginal guarantees for all marginals.
method Introduces relatively smart learning, which requires competition only with certifiable semi-supervised guarantees.
result One-Inclusion Graph learner is relatively smart up to squaring the sample complexity.
Research finds investors may lose from more diverse workplaces.
problem Investors' returns may be lower in more diverse companies.
method Examined D&I scores by Refinitiv in US and European markets.
result Investors may suffer lower returns for investing in more diverse companies.
The doubling conjecture for positive scalar curvature is proven under certain conditions.
problem Determining when a manifold with a specific boundary condition admits positive scalar curvature.
method Surgery techniques for positive scalar and mean curvature, and existence of area-minimizing hypersurfaces.
result The doubling conjecture holds true for manifolds with certain split conditions on fundamental groups.
Paper analyzes inclusive KL inference using Wasserstein gradient flows.
problem Analyzing inclusive KL inference with mathematical tools.
method Gradient flows derived from PDE analysis.
result Unified view of existing sampling algorithms as inclusive-KL inference.
Study shows 'Belt and Road' node cities boost digital finance in China.
problem Impact of 'Belt and Road' node cities on digital inclusive finance.
method Descriptive analysis, literature review, theoretical model, differential differential method.
result Establishment of 'Belt and Road' node cities promotes digital inclusive finance in China, with Internet development as a mediating variable.
New solutions for soft materials' nonlinear behavior in growing spheroidal inclusions.
problem Understanding soft materials' behavior under microstructure effects.
method Presented an accurate semi-inverse solution for isotropically growing spheroidal inclusions.
result Existence of a non-spherical asymptotic shape and associated asymptotic pressure (isomorphic pressure).
Study shows financial literacy, social capital, and financial tech positively impact financial inclusion of Indonesian students.
problem Financial literacy, social capital, and financial technology's impact on financial inclusion of Indonesian students.
method Quantitative research using questionnaires distributed to 100 students from 7 private colleges in Tangerang, Indonesia.
result Financial literacy, social capital, and financial technology have a positive and significant influence on financial inclusion.
Study shows solutions of differential inclusions are homotopy equivalent in -topology.
problem Homotopy properties of solutions in differential inclusions.
method Analyzes differential inclusion with specific assumptions on corank one distribution.
result Solutions are homotopy equivalent to loop spaces in -topology.
Inspired by the recent work of Chen-Stiénon-Xu on Atiyah classes associated to inclusions of Lie algebroids, we give a very simple criterium (in terms of those classes) for relative Poincaré-Birkhoff-Witt type results to hold. The tools we use (e.g. the first infinitesimal neighbourhood Lie algebroid) are straightforwa…
When looking at Bott's original proof of his periodicity theorem for the stable homotopy groups of the orthogonal and unitary groups, one sees in the background a differential geometric periodicity phenomenon. We show that this geometric phenomenon extends to the standard inclusion of the orthogonal group into the unit…
Study new bounds on TC of spaces with subgroup inclusions.
problem Lower bounds on TC of spaces with subgroup inclusions.
method Generalizes TC results from aspherical spaces to spaces with subgroup inclusions.
result Establishes new lower bounds on sequential TCs of aspherical spaces.
Paper presents a novel time series clustering algorithm for financial inclusion.
problem Difficulty in understanding consumer financial behavior without restrictive credit scoring.
method Developed a novel time series clustering algorithm.
result Allows institutions to offer unique financial products based on customer needs.
Paper develops an online covariance estimator for nonsmooth stochastic approximation problems.
problem Estimating covariance in nonsmooth, potentially non-monotone settings.
method Online batch-means covariance matrix estimator.
result Estimator achieves convergence rate of .
Regulating crypto and DeFi for inclusive economic advancement.
problem Innovative financial systems pose challenges to traditional regulatory frameworks.
method Formulating regulatory structures that balance innovation and consumer protection.
result Regulatory frameworks are essential for leveraging crypto and DeFi for inclusive economic growth.
Construct intrinsic Langevin dynamics for rigid inclusions on curved surfaces.
problem Stochastic dynamics of rigid inclusions on curved surfaces.
method Cartan's method of moving frames, Hamiltonian equations, intrinsic Langevin equations, Fokker-Planck equation.
result Extracted overdamped equations for accurate simulations of diffusion processes.
Novel algorithm reduces feature inclusion in online decision-making.
problem Optimizing decision-making for personalized user experiences with fairness.
method Online Batched Sequential Inclusion (OBSI) algorithm for sequential feature inclusion.
result OBSI outperforms other algorithms in terms of regret, relevance of features, and compute.
In this paper, we propose a verified numerical method for obtaining a sharp inclusion of the best constant for the embedding on bounded convex domain in . We estimate the best constant by computing the corresponding extremal function using a verified numerical com…
Hydrodynamic Coupling of Particle Inclusions Embedded in Curved Lipid Bilayer Membranescond-mat.soft
We develop theory and computational methods to investigate particle inclusions embedded within curved lipid bilayer membranes. We consider the case of spherical lipid vesicles where inclusion particles are coupled through (i) intramembrane hydrodynamics, (ii) traction stresses with the external and trapped solvent flui…
New algorithm for fast nonsmooth optimization with applications in image processing and machine learning.
problem Minimizing the sum of three convex functions with specific properties.
method PDDY algorithm, based on Davis-Yin splitting in a primal-dual product space.
result Sublinear and linear convergence rates in various scenarios, including strong convexity.
Extragradient-Type Methods with Last-Iterate Convergence Rates for Co-Hypomonotone Inclusionsmath.OC
Two accelerated extragradient methods converge at rate for co-hypomonotone inclusions.
problem Solving co-hypomonotone inclusions with sum of Lipschitz and multivalued operators.
method Developed two Nesterov's accelerated extragradient methods for co-hypomonotone inclusions.
result Achieve last-iterate convergence rates on the residual norm.
Neural random fields (NRFs), referring to a class of generative models that use neural networks to implement potential functions in random fields (a.k.a. energy-based models), are not new but receive less attention with slow progress. Different from various directed graphical models such as generative adversarial netwo…
The paper explores how the unit inclusion affects topological quantum field theories in non-semisimple categories.
problem Understanding the effects of unit inclusion in non-semisimple braided tensor categories on topological quantum field theories.
method Analyzes the dualizability of the unit inclusion morphism in Morita 4-category of braided tensor categories and applies the Cobordism Hypothesis.
result Shows that the unit inclusion in non-semisimple modular categories leads to non-compact relative 3D topological quantum field theories.
TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusioncs.LG
TransBoost improves financial inclusion by evaluating individual financial risk.
problem Evaluating individual financial risk for new users and companies with limited data.
method Combines tree-based models and kernel methods, with parallel tree structure and efficient weights updating.
result Outperforms other transfer learning algorithms in prediction accuracy and robustness.
The paper addresses monotonicity in machine learning models for fairness and accountability.
problem Ensuring fairness and accountability in transparent machine learning models.
method Study of three types of monotonicity (individual, weak pairwise, strong pairwise) and propose monotonic groves of neural additive models.
result Monotonic groves of neural additive models maintain transparency, accountability, and fairness.
Brunnian braids and the inclusion from double shuffle Lie algebra to Kashiwara-Vergne Lie algebramath.QA
Proof of injection from double shuffle to Kashiwara-Vergne Lie algebra.
problem Injecting double shuffle Lie algebra into Kashiwara-Vergne Lie algebra.
method Inclusion of brunnian braids group on different genus 0 surfaces, using lower central series of brunnian Lie algebras, and explicit links between maps.
result Injection of double shuffle Lie algebra into symmetric Kashiwara-Vergne Lie algebra.
New invariant identifies complex line arrangements with same combinatorics but different embeddings.
problem Identify Zariski pairs with same combinatorics but different line arrangements.
method Study inclusion map of boundary manifold to exterior, analyze homology classes, compute invariant using Sage.
result New invariant distinguishes line arrangements with same combinatorics but different embeddings.
Chordal graphs can be used to encode dependency models that are representable by both directed acyclic and undirected graphs. This paper discusses a very simple and efficient algorithm to learn the chordal structure of a probabilistic model from data. The algorithm is a greedy hill-climbing search algorithm that uses t…
In 2018, at the World Economic Forum in Davos it was presented a new countries' economic performance metric named the Inclusive Development Index (IDI) composed of 12 indicators. The new metric implies that countries might need to realize structural reforms for improving both economic expansion and social inclusion per…
In this paper the stability theorem of Borkar and Meyn is extended to include the case when the mean field is a differential inclusion. Two different sets of sufficient conditions are presented that guarantee the stability and convergence of stochastic recursive inclusions. Our work builds on the works of Benaim, Hofba…
Graph machine learning and Super-App data improve credit risk prediction for financial inclusion.
problem Improving credit risk prediction for financial inclusion.
method Two graph-based experiments using centrality, behavior, and transactionality features.
result Graph features enhance credit risk models, leading to more inclusive financial systems.
Two new algorithms optimize decentralized convex optimization with reduced communication rounds.
problem Decentralized minimization of smooth strongly convex functions in a network.
method Proposes two new algorithms based on accelerated Forward Backward methods.
result First algorithm is optimal in terms of communication rounds and gradient computations.
Probit Monotone BARTstat.ML
Probit Monotone BART estimates binary outcomes using monotonic functions.
problem Estimating conditional mean functions for binary outcomes with monotonicity constraints.
method Proposes a new BART variant that incorporates monotonicity constraints for binary outcomes.
result Allows for more precise estimation of monotonic functions in binary outcome models.
Identifying the flavour of neutral mesons production is one of the most important components needed in the study of time-dependent violation. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present an inclusive flavour-tagging algorithm as an upgrade of…
Monotone neural networks can approximate and interpolate functions efficiently.
problem Understanding the efficiency and expressiveness of monotone neural networks.
method Solving the monotone interpolation problem using depth-4 networks and comparing size bounds with arbitrary networks.
result Monotone neural networks can approximate and interpolate functions efficiently, but may require exponential size in high dimensions.
Improves k-NN for monotonic data with robustness against noise.
problem Class noise in real-life data violates monotonic constraints in k-NN.
method Monotonic Fuzzy k-NN (MonFkNN) with new fuzzy membership calculation.
result Significant accuracy improvements and robustness against monotonic noise.
Study maximal antipodal sets in exceptional symmetric spaces.
problem Classify maximal antipodal sets in exceptional symmetric spaces.
method Combining existing literature and new results, classify maximal antipodal sets.
result Complete classification of maximal antipodal sets in all exceptional compact symmetric spaces.
Study on existence of metrics in conformal geometry with constraints on Schouten tensor.
problem Existence of metrics conformal to a given Riemannian metric with constraints on Schouten tensor.
method Use differential inclusions and viscosity solutions to prove existence and uniqueness of metrics.
result Existence and uniqueness results for fully nonlinear eigenvalue problems for the Schouten tensor.
Study examines explainable machine learning for monotonic models, finding Integrated gradients better for strong monotonicity.
problem Applying explainable machine learning to science-informed models.
method Proposed axioms for monotonicity, tested Shapley value and Integrated gradients methods.
result Integrated gradients provides better explanations for strong monotonicity.
Scalar dynamic risk measures for univariate positions in continuous time are commonly represented as backward stochastic differential equations. In the multivariate setting, dynamic risk measures have been defined and studied as families of set-valued functionals in the recent literature. There are two possible extensi…