scholarly journals Supercaloric functions for the parabolic p-Laplace equation in the fast diffusion case

Author(s):  
Ratan Kr. Giri ◽  
Juha Kinnunen ◽  
Kristian Moring

AbstractWe study a generalized class of supersolutions, so-called p-supercaloric functions, to the parabolic p-Laplace equation. This class of functions is defined as lower semicontinuous functions that are finite in a dense set and satisfy the parabolic comparison principle. Their properties are relatively well understood for $$p\ge 2$$ p ≥ 2 , but little is known in the fast diffusion case $$1<p<2$$ 1 < p < 2 . Every bounded p-supercaloric function belongs to the natural Sobolev space and is a weak supersolution to the parabolic p-Laplace equation for the entire range $$1<p<\infty $$ 1 < p < ∞ . Our main result shows that unbounded p-supercaloric functions are divided into two mutually exclusive classes with sharp local integrability estimates for the function and its weak gradient in the supercritical case $$\frac{2n}{n+1}<p<2$$ 2 n n + 1 < p < 2 . The Barenblatt solution and the infinite point source solution show that both alternatives occur. Barenblatt solutions do not exist in the subcritical case $$1<p\le \frac{2n}{n+1}$$ 1 < p ≤ 2 n n + 1 and the theory is not yet well understood.

1992 ◽  
Vol 35 (4) ◽  
pp. 463-474 ◽  
Author(s):  
J. M. Borwein ◽  
M. Théra

AbstractWe provide vector analogues of the classical interpolation theorems for lower semicontinuous functions due to Dowker and to Hahn and Katetov-Tong.


2021 ◽  
Vol Volume 2 (Original research articles) ◽  
Author(s):  
Antonio Silveti-Falls ◽  
Cesare Molinari ◽  
Jalal Fadili

In this paper we propose and analyze inexact and stochastic versions of the CGALP algorithm developed in [25], which we denote ICGALP , that allow for errors in the computation of several important quantities. In particular this allows one to compute some gradients, proximal terms, and/or linear minimization oracles in an inexact fashion that facilitates the practical application of the algorithm to computationally intensive settings, e.g., in high (or possibly infinite) dimensional Hilbert spaces commonly found in machine learning problems. The algorithm is able to solve composite minimization problems involving the sum of three convex proper lower-semicontinuous functions subject to an affine constraint of the form Ax = b for some bounded linear operator A. Only one of the functions in the objective is assumed to be differentiable, the other two are assumed to have an accessible proximal operator and a linear minimization oracle. As main results, we show convergence of the Lagrangian values (so-called convergence in the Bregman sense) and asymptotic feasibility of the affine constraint as well as strong convergence of the sequence of dual variables to a solution of the dual problem, in an almost sure sense. Almost sure convergence rates are given for the Lagrangian values and the feasibility gap for the ergodic primal variables. Rates in expectation are given for the Lagrangian values and the feasibility gap subsequentially in the pointwise sense. Numerical experiments verifying the predicted rates of convergence are shown as well.


Sign in / Sign up

Export Citation Format

Share Document