Improved convergence of fixed-point methods using windowed Anderson acceleration.
problem Improving convergence of fixed-point methods for symmetric operators.
method Windowed Anderson acceleration for symmetric fixed-point iterations.
result Windowed Anderson acceleration improves convergence over standard fixed-point methods.
Research shows quadratic growth in derivative maxima for certain interval diffeos with parabolic fixed points.
problem Analyzing the growth of derivative maxima for C2 interval diffeomorphisms with parabolic fixed points. method Examining C2 diffeomorphisms with only parabolic fixed points, focusing on tangency and repelling behavior. result Maximal growth of derivative maxima is exactly quadratic for diffeomorphisms with a non-quadratic tangency to identity at a repelling fixed point.
Developed an efficient iterative algorithm for SVI model.
problem SVI model's optimizer's strong dependence on input starting point.
method Fixed-point and least-square optimizer.
result Convergence results for fixed-point iterative algorithm in certain situations.
Banach's fixed point theorem for contraction maps has been widely used to analyze the convergence of iterative methods in non-convex problems. It is a common experience, however, that iterative maps fail to be globally contracting under the natural metric in their domain, making the applicability of Banach's theorem li…
WaveFit uses fixed-point iteration to create high-quality neural vocoders.
problem Creating high-quality neural vocoders with fast inference.
method Integrates GANs' adversarial training into a DDPM-like iterative framework based on fixed-point iteration.
result WaveFit synthesizes speech with naturalness comparable to human speech, and is significantly faster than existing methods.
Improved stochastic Halpern iteration for fixed-point approximation in normed spaces.
problem Approximating fixed-points of nonexpansive and contractive operators in normed finite-dimensional spaces.
method Stochastic Halpern iteration with minibatch, analyzing oracle complexity.
result Improved oracle complexity for nonexpansive operators, with a lower bound of Ω(ε−3). Study on curvature image iterations converging to solutions of Minkowski problems.
problem Solving Lp Minkowski problems with prescribed data. method Iterations of curvature image operators Λpφ applied to convex bodies. result Iterations of curvature image operators converge to fixed points under certain conditions.
Neural networks with bounded weights converge to unique fixed points.
problem Convergence and fixed points of neural networks with bounded weights.
method Analysis of iterative neural networks in Hilbert spaces with a single mild condition on weights.
result Guaranteed convergence to a unique fixed point with a bound on its norm.
Interpreting gradient methods as fixed-point iterations, we provide a detailed analysis of those methods for minimizing convex objective functions. Due to their conceptual and algorithmic simplicity, gradient methods are widely used in machine learning for massive data sets (big data). In particular, stochastic gradien…
The paper analyzes when credal sets stabilize under iterative updates in machine learning.
problem When do credal sets stabilize under iterative updates in machine learning?
method Fixed-point theorems for credal set updates.
result The paper provides the first analysis of credal set stability.
Unified framework for solving fixed-point equations in deterministic and stochastic settings.
problem Solving fixed-point equations for seminorm-contractive operators in both deterministic and stochastic contexts.
method Fixed-point theorem and stochastic approximation analysis.
result Unified finite-sample bounds for various reinforcement learning algorithms.
Upper bounds on fixed points in PWL neural networks with hyperplane analysis.
problem Analyzing the number of fixed points in neural networks with PWL activation.
method Hyperplane arrangements to bound the number of fixed points.
result Upper bounds on the number of fixed points for PWL networks, showing exponential growth in layers.
New kernels from ELU and GELU networks reveal non-trivial fixed points.
problem Understanding fixed-point dynamics in deep neural networks with ELU and GELU activations.
method Deriving covariance functions and analyzing fixed-point dynamics of ELU and GELU networks.
result ELU and GELU networks exhibit non-trivial fixed-point dynamics, explaining implicit regularization in overparameterized models.
Convex message passing algorithms converge to a fixed point.
problem Understanding convergence properties of convex message passing methods.
method Proving convergence of coordinate descent applied to piecewise-affine convex objectives, and showing this applies to various message passing methods.
result The iterates converge to a fixed point of the method, and the algorithm terminates in a known number of iterations.
A point q in a contact manifold is called a translated point for a contactomorphism φ, with respect to some fixed contact form, if φ(q) and q belong to the same Reeb orbit and the contact form is preserved at q. The problem of existence of translated points is related to the chord conjecture and to the problem of leafw…
A new clustering framework using fixed points for data analysis.
problem Lack of unified understanding and application of clustering algorithms in data analysis.
method Restated model-based clustering using fixed point theory, iteratively constructing contraction maps to find cluster centers.
result Unified clustering framework reveals convergence mechanisms and interconnections among clustering algorithms.
Study efficient power iteration for tensor models, proving convergence under specific conditions.
problem Simultaneous alternating power iteration for fixed-order asymmetric rank-one spiked tensor models.
method Finite-iteration local theory, geometrically decaying transient, fixed-order multilinear noise event, warm-start mechanism.
result Convergence to the unique informative local fixed point under specific conditions.
Study compares methods for computing hypergradients in machine learning problems.
problem Computing exact hypergradients in machine learning is difficult.
method Investigates reverse mode iterative differentiation and approximate implicit differentiation methods.
result Unified analysis provides iteration complexity bounds and hierarchy of methods.
The Bass model is calibrated to vanilla options using a fixed-point equation.
problem Calibration of the Bass local volatility model to vanilla options.
method Solving a fixed-point equation to achieve calibration.
result Existence and uniqueness of the solution to the fixed-point equation, and linear convergence of the fixed-point iteration scheme.
Proposes a faster second-order method for MDPs.
problem Slow convergence of first-order value iteration methods in MDPs.
method Applies Newton-Raphson method to successive relaxation value iteration scheme.
result Second-order convergence and faster convergence to optimal solution.
Characterizes extreme points in polygon limit sets.
problem Identifying boundary points in polygon limit sets.
method Characterization through affine dilations and polygon vertices.
result Characterizes which points lie on the boundary of convex hull.
Belief propagation (BP) is an iterative method to perform approximate inference on arbitrary graphical models. Whether BP converges and if the solution is a unique fixed point depends on both the structure and the parametrization of the model. To understand this dependence it is interesting to find \emph{all} fixed poi…
This paper studies a valuation framework for financial contracts subject to reference and counterparty default risks with collateralization requirement. We propose a fixed point approach to analyze the mark-to-market contract value with counterparty risk provision, and show that it is a unique bounded and continuous fi…
Paper extends BIP to nilmanifold products and characterizes fixed points.
problem Lack of Bounded Index Property for fixed points in aspherical manifolds.
method Extended BIP to iterates and proved BIP_k for nilmanifold products.
result Proved BIP_k for certain nilmanifold products.
Solves capillary curvature problems for specific p values.
problem Capillary curvature problems for −n<p<1 and θ∈(0,2π). method Iterative scheme based on capillary Minkowski problem and capillary curvature image operators.
result Fixed points of capillary curvature image operators correspond to solutions of capillary Lp-Minkowski problem. Develops accelerated fixed-point methods with delayed oracles for scientific computing.
problem Approximating fixed points of nonexpansive operators.
method Combines Nesterov's acceleration and KM iteration with delayed inexact oracles.
result Establishes improved convergence rates for fixed-point approximation.
DeepFPC uses neural networks to recover sparse signals from quantized measurements.
problem Recovering sparse signals from quantized measurements.
method Unfolding the fixed-point continuation algorithm into a deep neural network.
result DeepFPC outperforms state-of-the-art algorithms in DOA estimation.
A number of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov random field. Belief propagation, an iterative message-passing algorithm, computes exactly such marginals when the underlying graph is a tree. But it has gained its popularity as …
We propose a novel method to accelerate Lloyd's algorithm for K-Means clustering. Unlike previous acceleration approaches that reduce computational cost per iterations or improve initialization, our approach is focused on reducing the number of iterations required for convergence. This is achieved by treating the assig…
New analysis of stochastic approximation with non-expansive mappings.
problem Finite-time analysis of two-time-scale stochastic approximation with non-expansive mappings.
method Studied two-time-scale stochastic approximation algorithms with non-expansive mappings and projection steps.
result Last-iterate mean square residual error decays at a rate O(1/k1/4−ε). FPI methods compute barycenters of Gaussian sets for various dissimilarity measures.
problem Efficiently compute barycenters of Gaussian sets for multiple dissimilarity measures.
method Fixed-Point Iterations (FPI) for several dissimilarity measures.
result FPI provides a useful toolbox for fusion/reduction of Gaussian sets.
Given any positive sequence (\{c_n\}_{n \in {\Bbb N}}), we construct orientation preserving homeomorphisms (f:{\Bbb R}^3 \to {\Bbb R}^3) such that (Fix(f)=Per(f)=\{0\}), (0) is Lyapunov stable and (\limsup \frac{|i(f^m, 0)|}{c_m}= \infty). We will use our results to discuss and to point out some strong differences with…
Model shows IRS procedure for health insurance tax credits can diverge, proposing a new bisection method.
problem IRS procedure for calculating health insurance tax credits diverges for some self-employed taxpayers.
method Proposed a bisection procedure to calculate appropriate premium tax credits for tax returns.
result The bisection procedure can calculate appropriate premium tax credits for a model of simple tax returns.
In this paper, we propose an implicit gradient descent algorithm for the classic k-means problem. The implicit gradient step or backward Euler is solved via stochastic fixed-point iteration, in which we randomly sample a mini-batch gradient in every iteration. It is the average of the fixed-point trajectory that is c…
New method for predicting neuron activity with unknown stimuli.
problem Statistical inference of neuron activity with missing data and unknown sources.
method Maximum likelihood estimation with fixed-point iteration.
result Model increases system likelihood and reveals neural connections.
Study optimal portfolio strategies with time-varying discount rates.
problem Optimizing portfolio decisions with a non-constant discount rate.
method Introduced subgame perfect strategies to handle time inconsistency, using fixed point iteration to find the utility-weighted discount rate.
result Subgame perfect strategies are equivalent to optimal strategies under certain utility function assumptions.
We study minimal harmonic maps g:C→SO(3)\SL(3,R), parameterized by polynomial cubic differentials P in the plane. The asymptotic structure of such a g is determined by a convex polygon Y(P) in RP2. We give a conjectural method for determining Y(P) by solving…
We characterize the price of an Asian option, a financial contract, as a fixed-point of a non-linear operator. In recent years, there has been interest in incorporating changes of regime into the parameters describing the evolution of the underlying asset price, namely the interest rate and the volatility, to model sud…
This work presents a fast and non-convex algorithm for robust subspace recovery. The data sets considered include inliers drawn around a low-dimensional subspace of a higher dimensional ambient space, and a possibly large portion of outliers that do not lie nearby this subspace. The proposed algorithm, which we refer t…
This paper computes fixed point Floer cohomology for Dehn twists on surfaces.
problem Computing fixed point Floer cohomology for Dehn twists.
method Developed tools for computing fixed point Floer cohomology and product for Dehn twists in all dimensions.
result Splitting of the product and differential into local and Morse-theoretic contributions.
Fast algorithm for shortest paths on data-manifolds.
problem Computing shortest paths on manifolds learned from data is challenging.
method Fixed-point iteration scheme for solving ODEs without requiring Jacobians.
result Significant improvements in speed and stability over existing methods.
This work studies the contraction coefficients of Schrödinger bridge problems in linear systems.
problem Optimally controlling the evolution of a system's state density over time.
method Analyzes and improves the convergence rates of dynamic Schrödinger systems via geometric and control-theoretic interpretations.
result New insights into improving computation of worst-case contraction coefficients by preconditioning.
Faster algorithms for solving multichain MDPs under average-reward criterion.
problem Navigating towards the best connected component in multichain MDPs.
method Developed algorithms to better solve the navigational subproblem, achieving faster convergence rates.
result Improved rates of convergence and sharper complexity measures for multichain MDPs.
New framework improves robustness of implicit neural networks.
problem Ill-posedness and convergence instability in implicit neural networks.
method NEMON framework based on contraction theory for ℓ∞ norm, including well-posedness condition, average iteration, and input-output Lipschitz constant regularization. result Improved accuracy and robustness of implicit models with smaller input-output Lipschitz bounds.
The paper finds optimal strategies for hedging in incomplete markets using derivatives.
problem Optimal static hedging in incomplete markets with two underlying assets and vanilla options.
method Formulated as a utility maximization problem, solved through variational methods and fixed point analysis.
result Semi-analytical solutions for exponential, power/logarithmic, and quadratic utilities, with convergence to a fixed point for exponential utility.
In this paper, we consider the stochastic iterative counterpart of the value iteration scheme wherein only noisy and possibly biased approximations of the Bellman operator are available. We call this counterpart as the approximate value iteration (AVI) scheme. Neural networks are often used as function approximators, i…
New approach for optimal stopping under model ambiguity, considering agent's attitude towards ambiguity.
problem Optimal stopping under model ambiguity and varying levels of ambiguity aversion.
method Introduces a time-inconsistent stopping problem with an α-maxmin nonlinear expectation and seeks subgame perfect equilibrium policies through fixed-point iterations. result Equilibrium stopping policies can be obtained through fixed-point iteration and vary based on an agent's ambiguity attitude.
Develops a reinforcement learning algorithm for learning deterministic equilibrium policies in time-inconsistent control problems.
problem Learning equilibrium policies in time-inconsistent control problems.
method Continuous-time model-free reinforcement learning algorithm using deterministic policy gradient approach.
result Learned equilibrium policies in general time-inconsistent control problems.