Speaker
Laurentiu Leuştean
University of Bucharest
Talks at this conference:
Tuesday, 16:30, J335 
Proof mining and the viscosity approximation method 
Authors: Paulo Firmino and Laurentiu Leuştean This talk presents an application of proof mining to the viscosity approximation method (VAM) for accretive operators in Banach spaces, studied recently by Xu et al. [5]. Proof mining is a research program concerned with the extraction, by using prooftheoretic techniques, of new quantitative and qualitative information from mathematical proofs. We refer to Kohlenbach’s textbook [2] for details on proof mining. Let \(X\) be a normed space and \(A:X\to 2^X\) be an accretive operator with a nonempty set of zeros. The VAMe iteration, a generalization of VAM obtained by adding error terms, is defined as follows: \(\text{VAMe} \qquad x_0=x\in X, \quad x_{n+1}=\alpha_n f(x_n) +(1\alpha_n)J_{\lambda_n}^Ax_n + e_n,\) where \(f:X\to X\) is an \(\alpha\)contraction for \(\alpha\in[0,1)\), \((\alpha_n)_{n\in\mathbb{N}}\) is a sequence in \([0,1]\), \((\lambda_n)_{n\in\mathbb{N}}\) is a sequence in \((0,\infty)\), \((e_n)_{n\in\mathbb{N}}\) is a sequence in \(X\), and, for every \(n\in\mathbb{N}\), \(J_{\lambda_n}^A\) is the resolvent of order \(\lambda_n\) of \(A\). In [1] we apply proof mining methods to obtain quantitative asymptotic regularity results for the VAMe iteration, providing uniform rates of asymptotic regularity, \(\left(J_{\lambda_n}^A\right)\)asymptotic regularity and, for all \(m\in\mathbb{N}\), \(J_{\lambda_m}^A\)asymptotic regularity of VAMe. For concrete instances of the parameter sequences, linear rates are computed by applying \cite[Lemma 2.8]{LeuPin23}, a slight variation of a lemma due to Sabach and Shtern [4]. Bibliography
