A survey of nonlinear robust optimization

S Leyffer, M Menickelly, T Munson… - INFOR: Information …, 2020 - Taylor & Francis
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 …

Provably correct automatic sub-differentiation for qualified programs

SM Kakade, JD Lee - Advances in neural information …, 2018 - proceedings.neurips.cc
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 …

Taylor subsumes scott, berry, kahn and plotkin

D Barbarossa, G Manzonetto - … of the ACM on Programming Languages, 2019 - dl.acm.org
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 …

Derivative-free robust optimization by outer approximations

M Menickelly, SM Wild - Mathematical Programming, 2020 - Springer
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 …

Quantifying the sources of volatility in the IFRS 9 impairments

YS Stander - South African Journal of Accounting Research, 2021 - Taylor & Francis
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 …

Learning sparsity-promoting regularizers using bilevel optimization

A Ghosh, M McCann, M Mitchell, S Ravishankar - SIAM Journal on Imaging …, 2024 - SIAM
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 …

Solving Constrained Piecewise Linear Optimization Problems by Exploiting the Abs-linear Approach

T Kreimeier - 2023 - edoc.hu-berlin.de
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 …

Simulation of piecewise smooth differential algebraic equations with application to gas networks

T Streubel - 2022 - edoc.hu-berlin.de
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 …

[HTML][HTML] Polyhedral DC decomposition and DCA optimization of piecewise linear functions

A Griewank, A Walther - Algorithms, 2020 - mdpi.com
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 …

Nonsmooth, Nonconvex Optimization Using Functional Encoding and Component Transition Information

F Luo - arXiv preprint arXiv:2404.16273, 2024 - arxiv.org
We have developed novel algorithms for optimizing nonsmooth, nonconvex functions in
which the nonsmoothness is caused by nonsmooth operators presented in the analytical …