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Found 6 papers, 1 papers with code
Optimization and Optimizers for Adversarial Robustness
Taking advantage of PWCF and other existing numerical algorithms, we further explore the distinct patterns in the solutions found for solving these optimization problems using various combinations of losses, perturbation models, and optimization algorithms.
NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning
Imposing explicit constraints is relatively new but increasingly pressing in deep learning, stimulated by, e. g., trustworthy AI that performs robust optimization over complicated perturbation sets and scientific applications that need to respect physical laws and constraints.
Optimization for Robustness Evaluation beyond $\ell_p$ Metrics
Empirical evaluation of deep learning models against adversarial attacks entails solving nontrivial constrained optimization problems.
NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning
GRANSO is among the first optimization solvers targeting general nonsmooth NCVX problems with nonsmooth constraints, but, as it is implemented in MATLAB and requires the user to provide analytical gradients, GRANSO is often not a convenient choice in machine learning (especially deep learning) applications.
Optimization-based parametric model order reduction via $\mathcal{H}_2\otimes\mathcal{L}_2$ first-order necessary conditions
In this paper, we generalize existing frameworks for $\mathcal{H}_2\otimes\mathcal{L}_2$-optimal model order reduction to a broad class of parametric linear time-invariant systems.
Optimization and Control Numerical Analysis Systems and Control Systems and Control Numerical Analysis 15A24, 46N10, 65K05, 65Y20, 93A15, 93B40
Fast Interpolation-based Globality Certificates for Computing Kreiss Constants and the Distance to Uncontrollability
Divide-and-conquer techniques have been proposed that reduce the work complexity to $\mathcal{O}(n^4)$ on average and $\mathcal{O}(n^5)$ in the worst case, but these variants are nevertheless still very expensive and can be numerically unreliable.
Optimization and Control Numerical Analysis Numerical Analysis
