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casadi / casadi

Star 963

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.

python c-plus-plus library modular optimization matlab mathematics nonlinear octave numerical-calculations scientific-computing derivatives code-generation parameter-estimation academic-project optimal-control symbolic-manipulation algorithmic-differentation nonlinear-programming
  • Updated May 6, 2022
  • C++
optimization-engine

alphaville / optimization-engine

Star 249
Open

Compiler warnings for generated code

1
michael-p
michael-p commented Apr 14, 2022

Dear all,

This isn't really a bug, more like a small annoyance, but given the superb quality of this crate I figured I'd report it anyway... :)

On recent nightly (rustc 1.62.0-nightly (34a6c9f26 2022-04-13)), the generated code produces a compiler warning:

warning: unused doc comment
  --> crates/blimp_mpc/path_follow/icasadi_path_follow/src/lib.rs:33:1
   |
33 |   /// C interface (
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good first issue

RLGC-Project / RLGC

Star 68
Open

Cannot test codes due to outdated documentation

3
ayansengupta17
ayansengupta17 commented Nov 12, 2020

Documentation for running codes:

source activate
cd RLGC/src/py
python trainIEEE39LoadSheddingAgent_discrete_action.py

Problems and custom fixes:

  1. There is no file folder src/ as py/
  2. I found "trainIEEE39LoadSheddingAgent_discrete_action.py " under examples/IEEE39_load_shedding
  3. Running the file "trainIEEE39LoadSheddingAgent_discrete_action.py" throws import
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help wanted good first issue question

wanxinjin / Pontryagin-Differentiable-Programming

Star 61

A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.

reinforcement-learning machine-learning-algorithms motion-planning dynamical-systems control-systems trajectory-optimization optimal-control system-identification imitation-learning inverse-reinforcement-learning end-to-end-learning differentiable-programming reinforcement-learning-environments learning-control pontryagin-maximum-principle
  • Updated Mar 18, 2022
  • Python

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