34 citations · 110 across the 14 of their papers we have counts for
14 papers · 1 filter
A provably convergent MM-GKS variant for large-scale inverse problems
Mirjeta Pasha, Eric de Sturler, Misha Kilmer
For high-quality images with sharp edges, a popular choice for edge-preserving regularization is using a general(ized) -norm of the gradient of the image. This can be imple…
Nonlinear RMM-GKS for Large-Scale Dynamic and Streaming Inverse Problems with Uncertain Forward Operators
Toluwani Okunola, Mirjeta Pasha, Misha E. Kilmer +2
Many practical imaging systems suffer from uncertainty in acquisition geometry -- such as projection angles in computed tomography or sensor positions in photoacoustic tomography -…
Recycling MMGKS for large-scale dynamic and streaming data
Mirjeta Pasha, Eric de Sturler, Misha E. Kilmer
Reconstructing high-quality images with sharp edges requires the use of edge-preserving constraints in the regularized form of the inverse problem. The use of the -norm on…
Subspace Recycling for Sequences of Shifted Systems with Applications in Image Recovery
Misha E. Kilmer, Eric de Sturler
For many applications involving a sequence of linear systems with slowly changing system matrices, subspace recycling, which exploits relationships among systems and reuses search…
Stopping Criteria for the Conjugate Gradient Algorithm in High-Order Finite Element Methods
Yichen Guo, Eric de Sturler, Tim Warburton
We consider stopping criteria that balance algebraic and discretization errors for the conjugate gradient algorithm applied to high-order finite element discretizations of Poisson…
Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR)
Ege Ozsar, Misha Kilmer, Eric Miller +2
We introduce PaLEnTIR, a significantly enhanced parametric level-set (PaLS) method addressing the restoration and reconstruction of piecewise constant objects. Our key contribution…
Randomized Approach to Nonlinear Inversion Combining Simultaneous Random and Optimized Sources and Detectors
Selin Aslan, Eric de Sturler, Misha E. Kilmer
In partial differential equations-based (PDE-based) inverse problems with many measurements, many large-scale discretized PDEs must be solved for each evaluation of the misfit or o…
Computing Reduced Order Models via Inner-Outer Krylov Recycling in Diffuse Optical Tomography
Meghan O'Connell, Misha E. Kilmer, Eric de Sturler +1
In nonlinear imaging problems whose forward model is described by a partial differential equation (PDE), the main computational bottleneck in solving the inverse problem is the nee…
Preconditioning Parametrized Linear Systems
Arielle Grim-McNally, Eric de Sturler, Serkan Gugercin
Preconditioners are generally essential for fast convergence in the iterative solution of linear systems of equations. However, the computation of a good preconditioner can be expe…
Recycling Krylov subspaces for CFD applications and a new hybrid recycling solver
Amit Amritkar, Eric de Sturler, Katarzyna Świrydowicz +2
We focus on robust and efficient iterative solvers for the pressure Poisson equation in incompressible Navier-Stokes problems. Preconditioned Krylov subspace methods are popular fo…
Recycling BiCGSTAB with an Application to Parametric Model Order Reduction
Kapil Ahuja, Peter Benner, Eric de Sturler +1
Krylov subspace recycling is a process for accelerating the convergence of sequences of linear systems. Based on this technique, the recycling BiCG algorithm has been developed rec…
Nonlinear Parametric Inversion using Interpolatory Model Reduction
Eric de Sturler, Serkan Gugercin, Misha E. Kilmer +3
Nonlinear parametric inverse problems appear in several prominent applications; one such application is Diffuse Optical Tomography (DOT) in medical image reconstruction. Such inver…
Recycling BiCG with an Application to Model Reduction
Kapil Ahuja, Eric de Sturler, Serkan Gugercin +1
Science and engineering problems frequently require solving a sequence of dual linear systems. Besides having to store only few Lanczos vectors, using the BiConjugate Gradient meth…
Dynamic Adaptive Mesh Refinement for Topology Optimization
Shun Wang, Eric de Sturler, Glaucio H. Paulino
We present an improved method for topology optimization with both adaptive mesh refinement and derefinement. Since the total volume fraction in topology optimization is usually mod…