Robust optimization (RO) has attracted much attention from the optimization community over
the past decade. RO is dedicated to solving optimization problems subject to uncertainty …

Abstract The\emph {Cheap Gradient Principle}~\citep {Griewank: 2008: EDP: 1455489}---the
computational cost of computing a $ d $-dimensional vector of partial derivatives of a scalar …

The speculative ambition of replacing the old theory of program approximation based on
syntactic continuity with the theory of resource consumption based on Taylor expansion and …

We develop an algorithm for minimax problems that arise in robust optimization in the
absence of objective function derivatives. The algorithm utilizes an extension of methods for …

The International Financial Reporting Standards (IFRS) 9 accounting standard gives rise to
impairments that are sensitive to the economic cycle. Rules around stage migration and the …

We present a gradient-based heuristic method for supervised learning of sparsity-promoting
regularizers for denoising signals and images. Sparsity-promoting regularization is a key …

This thesis presents an algorithm for solving finite-dimensional optimization problems with a
piecewise linear objective function and piecewise linear constraints. For this purpose, it is …

As of yet natural gas will remain as a bridging technology, but our gas grid infrastructure
does have a future in a post-fossil fuel era for the transportation of carbon-free produced …

For piecewise linear functions f: R n↦ R we show how their abs-linear representation can be
extended to yield simultaneously their decomposition into a convex f ˇ and a concave part f …

We have developed novel algorithms for optimizing nonsmooth, nonconvex functions in
which the nonsmoothness is caused by nonsmooth operators presented in the analytical …