A JuMP extension for Stochastic Dual Dynamic Programming
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Updated
Nov 28, 2024 - Julia
A JuMP extension for Stochastic Dual Dynamic Programming
An intuitive modeling interface for infinite-dimensional optimization problems.
Proximal algorithms for nonsmooth optimization in Julia
Macros and functions to work with DSGE models.
Adaptive importance sampling modification to MPPI
Algorithm and model experiments for robot motion planning. Implemented in Julia.
A package for solving Differential Dynamic Programming and trajectory optimization problems.
Model and solve optimal control problems in Julia
Robust and optimal design and analysis of linear control systems
Trajectory Optimization for Robot Arms
Julia Framework for Quantum Dynamics and Control
Quantum Optimal Control with Direct Collocation
A Julia package for constrained trajectory optimization using direct methods.
Julia interface to CasADi via PyCall
A tool to solve optimal control problem
A component of the SciML scientific machine learning ecosystem for optimal control
Neural ODEs as Feedback Policies for Nonlinear Optimal Control (IFAC 2023) https://doi.org/10.1016/j.ifacol.2023.10.1248
A tiny quantum optimal control library.
Fundamentals of the control-toolbox ecosystem
Gradient Ascent Pulse Engineering in Julia
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