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tiny-solver-rs

Warning! This project is still under development.

crate PyPI - Version PyPI - Python Version

Inspired by ceres-solver, tiny-solver, and minisam.

This is a general optimizer written in Rust, including bindings for Python. If you're familiar with ceres-solver or factor-graph optimizers, you'll find it very easy to use.

Installation

python

The python package can be installed directly from PyPI:

pip install tiny-solver

rust

cargo add tiny-solver

Current Features

  • Automatic Derivatives using num-dual
  • Sparse QR, Sparse Cholesky using faer
  • GaussNewtonOptimizer
  • Multithreading jacobian
  • loss function (Huber)
  • Define factor in python

TODO

  • LevenbergMarquardtOptimizer
  • information matrix

Benchmark

dataset tiny-solver gtsam minisam
m3500 161.1ms 130.7ms 123.6 ms

It's not extremely optimized, but it's easy to install and use.

Usage

Rust

// define your own Cost/Factor struct
// impl residual function
// and the jacobian will be auto generated
struct CustomFactor {}
impl tiny_solver::factors::Factor for CustomFactor {
    fn residual_func(
        &self,
        params: &[nalgebra::DVector<num_dual::DualDVec64>],
    ) -> nalgebra::DVector<num_dual::DualDVec64> {
        let x = &params[0][0];
        let y = &params[1][0];
        let z = &params[1][1];

        na::dvector![x + y.clone().mul(2.0) + z.clone().mul(4.0), y.mul(z)]
    }
}

fn main() {
    // init logger, `export RUST_LOG=trace` to see more log
    env_logger::init();

    // init problem (factor graph)
    let mut problem = tiny_solver::Problem::new();

    // add residual blocks (factors)
    // add residual x needs to be close to 3.0
    problem.add_residual_block(
        1,
        vec![("x".to_string(), 1)],
        Box::new(tiny_solver::factors::PriorFactor {
            v: na::dvector![3.0],
        }),
        None,
    );
    // add custom residual for x and yz
    problem.add_residual_block(
        2,
        vec![("x".to_string(), 1), ("yz".to_string(), 2)],
        Box::new(CustomFactor {}),
        None,
    );

    // the initial values for x is 0.7 and yz is [-30.2, 123.4]
    let initial_values = HashMap::<String, na::DVector<f64>>::from([
        ("x".to_string(), na::dvector![0.7]),
        ("yz".to_string(), na::dvector![-30.2, 123.4]),
    ]);

    // initialize optimizer
    let optimizer = tiny_solver::GaussNewtonOptimizer {};

    // optimize
    let result = optimizer.optimize(&problem, &initial_values, None);

    // result
    for (k, v) in result {
        println!("{}: {}", k, v);
    }
}

Python

import numpy as np
from tiny_solver import Problem, GaussNewtonOptimizer
from tiny_solver.factors import PriorFactor, PyFactor

# define custom cost function in python
# the trade off is the jacobian for the problem cannot be done in parallel
# because of gil
def cost(x: np.ndarray, yz: np.ndarray) -> np.ndarray:
    r0 = x[0] + 2 * yz[0] + 4 * yz[1]
    r1 = yz[0] * yz[0]
    return np.array([r0, r1])


def main():

    # initialize problem (factor graph)
    problem = Problem()

    # factor defined in python
    custom_factor = PyFactor(cost)
    problem.add_residual_block(
        2,
        [
            ("x", 1),
            ("yz", 2),
        ],
        custom_factor,
        None,
    )

    # prior factor import from rust
    prior_factor = PriorFactor(np.array([3.0]))
    problem.add_residual_block(1, [("x", 1)], prior_factor, None)

    # initial values
    init_values = {"x": np.array([0.7]), "yz": np.array([-30.2, 123.4])}

    # optimizer
    optimizer = GaussNewtonOptimizer()
    result_values = optimizer.optimize(problem, init_values)

    # result
    for k, v in result_values.items():
        print(f"{k}: {v}")


if __name__ == "__main__":
    main()

Example

Basic example

cargo run -r --example small_problem

M3500 dataset

m3500 dataset rust result.
git clone https://github.com/powei-lin/tiny-solver-rs.git
cd tiny-solver-rs

# run rust version
cargo run -r --example m3500_benchmar

# run python version
pip install tiny-solver matplotlib
python3 examples/python/m3500.py

Dependencies

~16–23MB
~376K SLoC