Clarabel.rs
Optimization-Python
| Clarabel.rs | Optimization-Python | |
|---|---|---|
| 3 | 1 | |
| 252 | 221 | |
| 4.0% | - | |
| 8.5 | 0.0 | |
| 5 days ago | over 2 years ago | |
| Rust | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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Clarabel.rs
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Announcing Clarabel : interior point solver for convex optimization
Source: https://github.com/oxfordcontrol/Clarabel.rs
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Convex Optimizations in Rust
Hello! Currently there is no modeling layer similar to CVXPY in Rust, but there is a new high quality open source interior point conic solver: https://github.com/oxfordcontrol/Clarabel.rs
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Convex optimisation in Rust
There is a cone program solver from the Oxford Control group: https://github.com/oxfordcontrol/Clarabel.rs
Optimization-Python
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What is the best DCA Strategy - Part IV (Dynamic DCA)
One approach to this problem is based on the Nobel prize winning Modern Portfolio Theory (MPT). In fact, there we can use pretty simple code available online: https://github.com/tirthajyoti/Optimization-Python/blob/master/Portfolio_optimization.ipynb. There is a one BIG difference between DCA and MPT though. Here, we do not want to do a one-time purchase and try to gain maximum profit. We are looking at a dual problem, where we want to purchase regularly, while aiming maximum accumulation.
What are some alternatives?
faer-rs - Linear algebra foundation for the Rust programming language
market-making-backtest - algo trading backtesting on BitMEX
portfolio_allocation_js - A JavaScript library to allocate and optimize financial portfolios.
cocp - Source code for the examples accompanying the paper "Learning convex optimization control policies."
JuMP.jl - Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
Optimus * 96 - Optimus is a mathematical programming library for Scala.
psi4numpy - Combining Psi4 and Numpy for education and development.
analisis-numerico-computo-cientifico - Análisis numérico y cómputo científico
minizinc-python - Access to all MiniZinc functionality directly from Python
notebooks - Optimization notebooks