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math.OC2024
A structured L-BFGS method with diagonal scaling and its application to image registration
Florian Mannel, Hari Om Aggrawal
We devise an L-BFGS method for optimization problems in which the objective is the sum of two functions, where the Hessian of the first function is computationally unavailable whil…
math.OC2024
A globalization of L-BFGS and the Barzilai-Borwein method for nonconvex unconstrained optimization
Florian Mannel
We present a modified limited memory BFGS (L-BFGS) method that converges globally and linearly for nonconvex objective functions. Its distinguishing feature is that it turns into L…
math.OC2023
A structured L-BFGS method and its application to inverse problems
Florian Mannel, Hari Om Aggrawal, Jan Modersitzki
Many inverse problems are phrased as optimization problems in which the objective function is the sum of a data-fidelity term and a regularization. Often, the Hessian of the fideli…