:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Schweidtmann, Artur M., Bongartz, Dominik, Grothe, Daniel, Kerkenhoff, Tim, Lin, Xiaopeng, Najman, Jaromil, Mitsos, Alexander
Format:	Preprint
Published:	2020
Subjects:	Optimization and Control Machine Learning 90C26, 90C30, 90C90, 68T01, 60-04
Online Access:	https://arxiv.org/abs/2005.10902
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Optimization with Trained Machine Learning Models Embedded
by: Schweidtmann, Artur M., et al.
Published: (2022)

Accelerating Deterministic Global Optimization via GPU-parallel Interval Arithmetic
by: Zhang, Hongzhen, et al.
Published: (2025)

A Globally Convergent Gradient Method with Momentum
by: Lapucci, Matteo, et al.
Published: (2024)

Norm-induced Cuts: Outer Approximation for Lipschitzian Constraint Functions
by: Göß, Adrian, et al.
Published: (2024)

Barrier Algorithms for Constrained Non-Convex Optimization
by: Dvurechensky, Pavel, et al.
Published: (2024)

Riemannian Gradient Method with Momentum
by: Leggio, Filippo, et al.
Published: (2026)

Riemannian Adaptive Regularized Newton Methods with Hölder Continuous Hessians
by: Zhang, Chenyu, et al.
Published: (2023)

cuHALLaR: A GPU Accelerated Low-Rank Augmented Lagrangian Method for Large-Scale Semidefinite Programming
by: Aguirre, Jacob M., et al.
Published: (2025)

Clash of MINLP Relaxations: Piecewise Linear vs. Global Parabolic
by: Göß, Adrian
Published: (2026)

Parabolic Approximation & Relaxation for MINLP
by: Göß, Adrian, et al.
Published: (2024)

Optimization over convex polyhedra via Hadamard parametrizations
by: Tang, Tianyun, et al.
Published: (2024)

A Surrogate Value Function Formulation for Bilevel Optimization
by: Xu, Mengwei, et al.
Published: (2025)

The global convergence time of stochastic gradient descent in non-convex landscapes: Sharp estimates via large deviations
by: Azizian, Waïss, et al.
Published: (2025)

A low-rank augmented Lagrangian method for large-scale semidefinite programming based on a hybrid convex-nonconvex approach
by: Monteiro, Renato D. C., et al.
Published: (2024)

Lagrangian Reformulation for Nonconvex Optimization: Tailoring Problems to Specialized Solvers
by: Quintero, Rodolfo A., et al.
Published: (2024)

Derivative-Free Bilevel Optimization with Inexact Lower-Level Solutions
by: Cesaroni, Edoardo, et al.
Published: (2026)

Delayed Feedback in Online Non-Convex Optimization: A Non-Stationary Approach with Applications
by: Lara, Felipe, et al.
Published: (2024)

Combining Gradient Information and Primitive Directions for High-Performance Mixed-Integer Optimization
by: Lapucci, Matteo, et al.
Published: (2024)

Improving Convergence Guarantees of Random Subspace Second-order Algorithm for Nonconvex Optimization
by: Higuchi, Rei, et al.
Published: (2024)

On Tackling High-Dimensional Nonconvex Stochastic Optimization via Stochastic First-Order Methods with Non-smooth Proximal Terms and Variance Reduction
by: Xie, Yue, et al.
Published: (2025)

Optimal Subgradient Methods for Lipschitz Convex Optimization with Error Bounds
by: Wang, Alex L.
Published: (2025)

Forward-Backward algorithms for weakly convex problems
by: Bednarczuk, Ewa, et al.
Published: (2023)

Oracle complexities of augmented Lagrangian methods for nonsmooth manifold optimization
by: Deng, Kangkang, et al.
Published: (2024)

Solving Sparse MIQCQPs: Application to the Unit Commitment Problem with ACOPF Constraints
by: Gómez-Casares, Ignacio, et al.
Published: (2025)

What is the long-run distribution of stochastic gradient descent? A large deviations analysis
by: Azizian, Waïss, et al.
Published: (2024)

Depth-first directional search for nonconvex optimization
by: Zhang, Yuxuan, et al.
Published: (2024)

Projected Gradient Methods with Momentum
by: Lapucci, Matteo, et al.
Published: (2026)

Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models
by: Lapucci, Matteo, et al.
Published: (2024)

Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems
by: Lapucci, Matteo, et al.
Published: (2024)

A Stochastic Quasi-Newton Method in the Absence of Common Random Numbers
by: Menickelly, Matt, et al.
Published: (2023)

Benign landscapes of low-dimensional relaxations for orthogonal synchronization on general graphs
by: McRae, Andrew D., et al.
Published: (2023)

Hidden convexity of quadratic systems and its application to quadratic programming
by: Huy, Nguyen Quang, et al.
Published: (2026)

A minimal face constant rank constraint qualification for reducible conic programming
by: Andreani, Roberto, et al.
Published: (2023)

Effective Front-Descent Algorithms with Convergence Guarantees
by: Lapucci, Matteo, et al.
Published: (2024)

Projection-based curve pattern search for black-box optimization over smooth convex sets
by: Jia, Xiaoxi, et al.
Published: (2025)

Penalty decomposition derivative free method for the minimization of partially separable functions over a convex feasible set
by: Cecere, Francesco, et al.
Published: (2025)

Bound tightening in lifted formulations: (sub)solver-dependent impact on performance in RLT-based algorithms
by: González-Díaz, Julio, et al.
Published: (2025)

On convergence of a $q$-random coordinate constrained algorithm for non-convex problems
by: Ghaffari-Hadigheh, Alireza, et al.
Published: (2022)

Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives
by: Huong, Vu Thi, et al.
Published: (2025)

Complexity of Adagrad and other first-order methods for nonconvex optimization problems with bounds constraints
by: Gratton, Serge, et al.
Published: (2024)