A bi-level model and hybrid heuristic algorithm for the optimal location of prefabricated building industrial park
Optimal location for prefabricated building industrial park (PBIP) can significantly reduce the
logistic cost, delivery time and environmental pollution of prefabricated and modular …
logistic cost, delivery time and environmental pollution of prefabricated and modular …
[HTML][HTML] First-order methods for convex optimization
First-order methods for solving convex optimization problems have been at the forefront of
mathematical optimization in the last 20 years. The rapid development of this important class …
mathematical optimization in the last 20 years. The rapid development of this important class …
Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions
A Carderera, M Besançon… - Advances in Neural …, 2021 - proceedings.neurips.cc
Generalized self-concordance is a key property present in the objective function of many
important learning problems. We establish the convergence rate of a simple Frank-Wolfe …
important learning problems. We establish the convergence rate of a simple Frank-Wolfe …
Scalable Frank–Wolfe on generalized self-concordant functions via simple steps
A Carderera, M Besançon, S Pokutta - SIAM Journal on Optimization, 2024 - SIAM
Generalized self-concordance is a key property present in the objective function of many
important learning problems. We establish the convergence rate of a simple Frank–Wolfe …
important learning problems. We establish the convergence rate of a simple Frank–Wolfe …
Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging
Consider the problem of minimizing an expected logarithmic loss over either the probability
simplex or the set of quantum density matrices. This problem includes tasks such as solving …
simplex or the set of quantum density matrices. This problem includes tasks such as solving …
An Away-Step Frank-Wolfe Method for Minimizing Logarithmically-Homogeneous Barriers
R Zhao - arXiv preprint arXiv:2305.17808, 2023 - arxiv.org
We present and analyze an away-step Frank-Wolfe method for the convex optimization
problem ${\min} _ {x\in\mathcal {X}}\; f (\mathsf {A} x)+\langle {c},{x}\rangle $, where $ f $ is a …
problem ${\min} _ {x\in\mathcal {X}}\; f (\mathsf {A} x)+\langle {c},{x}\rangle $, where $ f $ is a …
Stochastic Bergman Proximal Gradient Method Revisited: Kernel Conditioning and Painless Variance Reduction
J Zhang - arXiv preprint arXiv:2401.03155, 2024 - arxiv.org
We investigate Bregman proximal gradient (BPG) methods for solving nonconvex composite
stochastic optimization problems. Instead of the standard gradient Lipschitz continuity (GLC) …
stochastic optimization problems. Instead of the standard gradient Lipschitz continuity (GLC) …
An inexact frank-wolfe algorithm for composite convex optimization involving a self-concordant function
In this paper, we consider Frank-Wolfe-based algorithms for composite convex optimization
problems with objective involving a logarithmically-homogeneous, self-concordant functions …
problems with objective involving a logarithmically-homogeneous, self-concordant functions …
Hessian barrier algorithms for non-convex conic optimization
P Dvurechensky, M Staudigl - Mathematical Programming, 2024 - Springer
A key problem in mathematical imaging, signal processing and computational statistics is
the minimization of non-convex objective functions that may be non-differentiable at the …
the minimization of non-convex objective functions that may be non-differentiable at the …
Second-order conditional gradient sliding
A Carderera, S Pokutta - arXiv preprint arXiv:2002.08907, 2020 - arxiv.org
Constrained second-order convex optimization algorithms are the method of choice when a
high accuracy solution to a problem is needed, due to their local quadratic convergence …
high accuracy solution to a problem is needed, due to their local quadratic convergence …