A library for differentiable nonlinear optimization
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Updated
Jan 15, 2024 - Python
A library for differentiable nonlinear optimization
Bilevel Optimization Library in Python for Multi-Task and Meta Learning
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming (ICML'21)
Example code for paper "Bilevel Optimization: Nonasymptotic Analysis and Faster Algorithms"
MetaStyle: Three-Way Trade-Off Among Speed, Flexibility, and Quality in Neural Style Transfer
PyTorch implementation of "STNs" and "Delta-STNs".
Coresets via Bilevel Optimization
Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
Benchmark for bi-level optimization solvers
JuMP-based toolbox for solving bilevel optimization problems
Proposed a mathematical model for optimizing the profits and emissions while setting dynamic prices of electricity. A bilevel & multi-objective model is proposed for maximizing profits of retailer, minimizing the emissions produced, & minimizing the total cost of customers.
Example Code for paper "Provably Faster Algorithms for Bilevel Optimization"
Deep Bilevel Learning. In ECCV, 2018.
Implementation and examples from Trajectory Optimization with Optimization-Based Dynamics https://arxiv.org/abs/2109.04928
Extended Mathematical Programming in Julia
Implementations of the algorithms described in the paper: On the Convergence Theory for Hessian-Free Bilevel Algorithms.
COMBINATORIAL OPTIMIZATION & NETWORK ANALYSIS - AUT- Professor: FARNAZ HOOSHMAND KHALIGH
In this repository, we implement Targeted Meta-Learning (or Targeted Data-driven Regularization) architecture for training machine learning models with biased data.
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