8 papers
Randomized Krylov-Projected Iterated Tikhonov Regularization for Large-Scale Ill-posed Problems Under A Posteriori Stopping Rule
Ravi Verma, Harshit Bajpai, Ankik Kumar Giri
We introduce two novel randomized iterative regularization frameworks, termed \texttt{RIGKT} and \texttt{RIAT}, for solving large-scale linear ill-posed inverse problems governed b…
On the convergence of an adaptive denoiser driven iterative regularization with early stopping
Harshit Bajpai, Ankik Kumar Giri, Tim Jahn +1
Solving inverse problems requires appropriate regularization techniques to ensure well-posedness and stability. In recent years, denoiser-driven methods have emerged as effective r…
Graph Laplacian assisted regularization method under noise level free heuristic and statistical stopping rule
Harshit Bajpai, Ankik Kumar Giri
In this work, we address the solution of both linear and nonlinear ill-posed inverse problems by developing a novel graph-based regularization framework, where the regularization t…
On the convergence of iterative regularization method assisted by the graph Laplacian with early stopping
Harshit Bajpai, Gaurav Mittal, Ankik Kumar Giri
We present a data-assisted iterative regularization method for solving ill-posed inverse problems. The proposed approach, termed \texttt{IRMGL+\(Ψ\)}, integrates classical iterati…
Hanke-Raus heuristic rule for iteratively regularized stochastic gradient descent
Harshit Bajpai, Gaurav Mittal, Ankik Kumar Giri
Over the past decade, stochastic algorithms have emerged as scalable and efficient tools for solving large-scale ill-posed inverse problems by randomly selecting subsets of equatio…
Well-posedness and large time behavior of a size-structured growth-coagulation-fragmentation model
Saroj Si, Ankik Kumar Giri
The existence and uniqueness of weak solutions to a size-structured growth-coagulation-fragmentation (GCF) equation with a renewal boundary condition are shown for a class of unbou…