Example: Fitting a spring-mass model to data

In this example we'll fit a damped spring-mass system to some synthetic data (using the model to generate the data). The system consists of a mass \(m\) attached to a spring with spring constant \(k\), and a damping force proportional to the velocity of the mass with damping coefficient \(c\).

\[ \begin{align*} \frac{dx}{dt} &= v \\ \frac{dv}{dt} &= -\frac{k}{m} x - \frac{c}{m} v \end{align*} \]

where \(v = \frac{dx}{dt}\) is the velocity of the mass.

We'll use the argmin crate to perform the optimisation. To hold the synthetic data and the model, we'll create a struct Problem like so

use argmin::{
    core::{observers::ObserverMode, CostFunction, Executor, Gradient},
    solver::{linesearch::MoreThuenteLineSearch, quasinewton::LBFGS},
};
use argmin_observer_slog::SlogLogger;
use diffsol::{
    AdjointOdeSolverMethod, DenseMatrix, DiffSl, Matrix, MatrixCommon, NalgebraMat, NalgebraVec,
    OdeBuilder, OdeEquations, OdeSolverMethod, OdeSolverProblem, OdeSolverState, Op, Scale, Vector,
    VectorCommon, VectorViewMut,
};
use std::cell::RefCell;

type M = NalgebraMat<f64>;
type V = NalgebraVec<f64>;
type T = f64;
type LS = diffsol::NalgebraLU<f64>;
type CG = diffsol::LlvmModule;
type Eqn = DiffSl<M, CG>;

struct Problem {
    ys_data: M,
    ts_data: Vec<T>,
    problem: RefCell<OdeSolverProblem<Eqn>>,

To use argmin we need to specify traits giving the loss function and its gradient. In this case we'll define a loss function equal to the sum of squares error between the model output and the synthetic data.

$$ \text{loss} = \sum_i (y_i(p) - \hat{y}_i)^2 $$

where \(y_i(p)\) is the model output as a function of the parameters \(p\), and \(\hat{y}_i\) is the observed data at time index \(i\).

impl CostFunction for Problem {
    type Output = T;
    type Param = Vec<T>;

    fn cost(&self, param: &Self::Param) -> Result<Self::Output, argmin_math::Error> {
        let mut problem = self.problem.borrow_mut();
        let context = problem.eqn().context().clone();
        problem
            .eqn_mut()
            .set_params(&V::from_vec(param.clone(), context));
        let mut solver = problem.bdf::<LS>().unwrap();
        let ys = match solver.solve_dense(&self.ts_data) {
            Ok(ys) => ys,
            Err(_) => return Ok(f64::MAX / 1000.),
        };
        let loss = ys
            .inner()
            .column_iter()
            .zip(self.ys_data.inner().column_iter())
            .map(|(a, b)| (a - b).norm_squared())

The gradient of this cost function with respect to the model outputs \(y_i\) is

$$ \frac{\partial \text{loss}}{\partial y_i} = 2 (y_i(p) - \hat{y}_i) $$

We can calculate this using DiffSol's adjoint sensitivity analysis functionality. First we solve the forwards problem, generating a checkpointing struct. Using the forward solution we can then calculate \(\frac{\partial loss}{\partial y_i}\) for each time point, and then pass this into the adjoint backwards pass to calculate the gradient of the cost function with respect to the parameters.

        Ok(loss)
    }
}

impl Gradient for Problem {
    type Gradient = Vec<T>;
    type Param = Vec<T>;

    fn gradient(&self, param: &Self::Param) -> Result<Self::Gradient, argmin_math::Error> {
        let mut problem = self.problem.borrow_mut();
        let context = problem.eqn().context().clone();
        problem
            .eqn_mut()
            .set_params(&V::from_vec(param.clone(), context));
        let mut solver = problem.bdf::<LS>().unwrap();
        let (c, ys) = match solver.solve_dense_with_checkpointing(&self.ts_data, None) {
            Ok(ys) => ys,
            Err(_) => return Ok(vec![f64::MAX / 1000.; param.len()]),
        };
        let mut g_m = M::zeros(2, self.ts_data.len(), problem.eqn().context().clone());
        for j in 0..g_m.ncols() {
            let g_m_i = (ys.column(j) - self.ys_data.column(j)) * Scale(2.0);
            g_m.column_mut(j).copy_from(&g_m_i);
        }
        let adjoint_solver = problem.bdf_solver_adjoint::<LS, _>(c, Some(1)).unwrap();

In our main function we'll create the model, generate some synthetic data, and then call argmin to fit the model to the data.

        match adjoint_solver.solve_adjoint_backwards_pass(self.ts_data.as_slice(), &[&g_m]) {
            Ok(soln) => Ok(soln.as_ref().sg[0]
                .inner()
                .iter()
                .copied()
                .collect::<Vec<_>>()),
            Err(_) => Ok(vec![f64::MAX / 1000.; param.len()]),
        }
    }
}

pub fn main() {
    let (k_true, c_true) = (1.0, 0.1);
    let t_data = (0..101)
        .map(|i| f64::from(i) * 40. / 100.)
        .collect::<Vec<f64>>();
    let problem = OdeBuilder::<M>::new()
        .p([k_true, c_true])
        .sens_atol([1e-6])
        .sens_rtol(1e-6)
        .out_atol([1e-6])
        .out_rtol(1e-6)
        .build_from_diffsl(
            "
        in = [k, c]
        k { 1.0 } m { 1.0 } c { 0.1 }
        u_i {
            x = 1,
            v = 0,
        }
        F_i {
            v,
            -k/m * x - c/m * v,
        }
        ",
        )
        .unwrap();
    let mut solver = problem.bdf::<LS>().unwrap();
    let ys_data = solver.solve_dense(&t_data).unwrap();

    let cost = Problem {
        ys_data,
        ts_data: t_data,
        problem: RefCell::new(problem),
    };

    let init_param = vec![k_true - 0.1, c_true - 0.01];

    let linesearch = MoreThuenteLineSearch::new().with_c(1e-4, 0.9).unwrap();
    let solver = LBFGS::new(linesearch, 7);
    let res = Executor::new(cost, solver)
        .configure(|state| state.param(init_param))
        .add_observer(SlogLogger::term(), ObserverMode::Always)
        .run()
        .unwrap();

    // print result
    println!("{}", res);
    // Best parameter vector
    let best = res.state().best_param.as_ref().unwrap();
    println!("Best parameter vector: {:?}", best);
    println!("True parameter vector: {:?}", vec![k_true, c_true]);
}

Mar 05 15:12:51.068 INFO L-BFGS
Mar 05 15:12:53.667 INFO iter: 0, cost: 6.099710479003417, best_cost: 6.099710479003417, gradient_count: 14, cost_count: 13, gamma: 1, time: 2.598805685
Mar 05 15:12:54.246 INFO iter: 1, cost: 1.9513388387772255, best_cost: 1.9513388387772255, gradient_count: 18, cost_count: 16, time: 0.579365776, gamma: 0.0009218600784406668
Mar 05 15:12:54.679 INFO iter: 2, cost: 1.1328003486802616, best_cost: 1.1328003486802616, gradient_count: 21, cost_count: 18, gamma: 0.0011158173475820988, time: 0.432772159
Mar 05 15:12:55.100 INFO iter: 3, cost: 0.36245408149937774, best_cost: 0.36245408149937774, gradient_count: 24, cost_count: 20, time: 0.421368339, gamma: 0.0010683972634626152
Mar 05 15:12:55.473 INFO iter: 4, cost: 0.005661451144141899, best_cost: 0.005661451144141899, gradient_count: 27, cost_count: 22, gamma: 0.0010337960155067532, time: 0.372749194
Mar 05 15:12:55.657 INFO iter: 5, cost: 0.0001534604284670027, best_cost: 0.0001534604284670027, gradient_count: 29, cost_count: 23, gamma: 0.0005519136139557582, time: 0.183717262
Mar 05 15:12:55.811 INFO iter: 6, cost: 0.000017178666309946563, best_cost: 0.000017178666309946563, gradient_count: 31, cost_count: 24, time: 0.154246656, gamma: 0.0005222593123731191
Mar 05 15:12:55.934 INFO iter: 7, cost: 0.0000011504081912133204, best_cost: 0.0000011504081912133204, gradient_count: 33, cost_count: 25, gamma: 0.0005839951848538406, time: 0.123233143
Mar 05 15:12:55.999 INFO iter: 8, cost: 0.0000000057304906811474396, best_cost: 0.0000000057304906811474396, gradient_count: 35, cost_count: 26, time: 0.064562531, gamma: 0.0004953781862470435
Mar 05 15:12:56.040 INFO iter: 9, cost: 0.00000000014827166483234088, best_cost: 0.00000000014827166483234088, gradient_count: 37, cost_count: 27, gamma: 0.0004739141598396127, time: 0.041539938
Mar 05 15:12:56.089 INFO iter: 10, cost: 0.00000000005665660355834637, best_cost: 0.00000000005665660355834637, gradient_count: 39, cost_count: 28, time: 0.048698991, gamma: 0.0006574550747061086
Mar 05 15:12:56.150 INFO iter: 11, cost: 0.00000000004046321552763034, best_cost: 0.00000000004046321552763034, gradient_count: 42, cost_count: 30, gamma: 0.0007768025353897974, time: 0.06118116
Mar 05 15:12:56.230 INFO iter: 12, cost: 0.000000000028544950162250156, best_cost: 0.000000000028544950162250156, gradient_count: 45, cost_count: 32, time: 0.079473263, gamma: 0.0007211431129919831
Mar 05 15:12:56.310 INFO iter: 13, cost: 0.000000000019824882126122364, best_cost: 0.000000000019824882126122364, gradient_count: 48, cost_count: 34, gamma: 0.0005276999999419798, time: 0.079908383
Mar 05 15:12:56.399 INFO iter: 14, cost: 0.000000000014773791668031016, best_cost: 0.000000000014773791668031016, gradient_count: 51, cost_count: 36, time: 0.088933521, gamma: 0.0006137808250157392
Mar 05 15:12:56.488 INFO iter: 15, cost: 0.000000000011918443921135866, best_cost: 0.000000000011918443921135866, gradient_count: 54, cost_count: 38, time: 0.088925265, gamma: 0.0006964726153446881
Mar 05 15:12:56.577 INFO iter: 16, cost: 0.000000000009847613120347547, best_cost: 0.000000000009847613120347547, gradient_count: 57, cost_count: 40, gamma: 0.0006788190120544423, time: 0.088873097
Mar 05 15:12:56.666 INFO iter: 17, cost: 0.000000000008250422213735562, best_cost: 0.000000000008250422213735562, gradient_count: 60, cost_count: 42, gamma: 0.0007147657772943421, time: 0.089253633
Mar 05 15:12:56.735 INFO iter: 18, cost: 0.000000000006963535307175679, best_cost: 0.000000000006963535307175679, gradient_count: 63, cost_count: 44, gamma: 0.0007522010953601424, time: 0.069181779
Mar 05 15:12:56.794 INFO iter: 19, cost: 0.000000000005843346318988485, best_cost: 0.000000000005843346318988485, gradient_count: 66, cost_count: 46, gamma: 0.0008115415177531896, time: 0.05931185
Mar 05 15:12:56.875 INFO iter: 20, cost: 0.000000000005668499496383206, best_cost: 0.000000000005668499496383206, gradient_count: 70, cost_count: 49, gamma: 0.0007981283294910353, time: 0.080732485
Mar 05 15:12:56.966 INFO iter: 21, cost: 0.000000000005235587953062947, best_cost: 0.000000000005235587953062947, gradient_count: 74, cost_count: 52, gamma: 0.0007824334700764565, time: 0.090509819
Mar 05 15:12:57.025 INFO iter: 22, cost: 0.000000000005176697246946799, best_cost: 0.000000000005176697246946799, gradient_count: 77, cost_count: 54, time: 0.05927997, gamma: 0.0007160320701376257
Mar 05 15:12:57.084 INFO iter: 23, cost: 0.000000000005123431411964367, best_cost: 0.000000000005123431411964367, gradient_count: 80, cost_count: 56, time: 0.059282524, gamma: 0.0006691254860074899
Mar 05 15:12:57.145 INFO iter: 24, cost: 0.000000000004888671439469577, best_cost: 0.000000000004888671439469577, gradient_count: 83, cost_count: 58, time: 0.060525746, gamma: 0.0006870970887038928
Mar 05 15:12:57.205 INFO iter: 25, cost: 0.000000000004640183634642808, best_cost: 0.000000000004640183634642808, gradient_count: 86, cost_count: 60, time: 0.060494317, gamma: 0.0006938418390339419
Mar 05 15:12:57.266 INFO iter: 26, cost: 0.000000000004202140058725012, best_cost: 0.000000000004202140058725012, gradient_count: 89, cost_count: 62, gamma: 0.0008074272439861259, time: 0.060721945
Mar 05 15:12:57.327 INFO iter: 27, cost: 0.000000000003819065447066373, best_cost: 0.000000000003819065447066373, gradient_count: 92, cost_count: 64, time: 0.060539541, gamma: 0.000745089693258659
Mar 05 15:12:57.388 INFO iter: 28, cost: 0.0000000000034723813904317016, best_cost: 0.0000000000034723813904317016, gradient_count: 95, cost_count: 66, time: 0.061195346, gamma: 0.0006679419374299279
Mar 05 15:12:57.449 INFO iter: 29, cost: 0.0000000000029793598830196132, best_cost: 0.0000000000029793598830196132, gradient_count: 98, cost_count: 68, gamma: 0.0006803520819495024, time: 0.061207339
Mar 05 15:12:57.510 INFO iter: 30, cost: 0.000000000002000300316746391, best_cost: 0.000000000002000300316746391, gradient_count: 101, cost_count: 70, gamma: 0.0007707919521852348, time: 0.061103644
Mar 05 15:12:57.571 INFO iter: 31, cost: 0.0000000000013081231359178465, best_cost: 0.0000000000013081231359178465, gradient_count: 104, cost_count: 72, gamma: 0.0007594375561408552, time: 0.061080561
Mar 05 15:12:57.632 INFO iter: 32, cost: 0.0000000000009129669554476711, best_cost: 0.0000000000009129669554476711, gradient_count: 107, cost_count: 74, gamma: 0.0007714623407119796, time: 0.061010138
Mar 05 15:12:57.693 INFO iter: 33, cost: 0.0000000000006624988392852611, best_cost: 0.0000000000006624988392852611, gradient_count: 110, cost_count: 76, time: 0.060805933, gamma: 0.0006929806628651383
Mar 05 15:12:57.732 INFO iter: 34, cost: 0.0000000000006457742400885126, best_cost: 0.0000000000006457742400885126, gradient_count: 112, cost_count: 77, gamma: 0.0006611075118236918, time: 0.038888894
Mar 05 15:12:57.771 INFO iter: 35, cost: 0.0000000000004844629418810476, best_cost: 0.0000000000004844629418810476, gradient_count: 114, cost_count: 78, time: 0.038867624, gamma: 0.0006746920875937051
Mar 05 15:12:57.832 INFO iter: 36, cost: 0.00000000000021331454699420582, best_cost: 0.00000000000021331454699420582, gradient_count: 117, cost_count: 80, time: 0.060733748, gamma: 0.0006901151177651382
Mar 05 15:12:57.870 INFO iter: 37, cost: 0.0000000000001604124255790092, best_cost: 0.0000000000001604124255790092, gradient_count: 119, cost_count: 81, gamma: 0.0007678508731651804, time: 0.038720877
Mar 05 15:12:57.909 INFO iter: 38, cost: 0.00000000000006736683702894452, best_cost: 0.00000000000006736683702894452, gradient_count: 121, cost_count: 82, time: 0.038724956, gamma: 0.0007450395288400451
Mar 05 15:12:57.948 INFO iter: 39, cost: 0.000000000000033549027916251836, best_cost: 0.000000000000033549027916251836, gradient_count: 123, cost_count: 83, gamma: 0.0007953280906606892, time: 0.038636279
Mar 05 15:12:57.986 INFO iter: 40, cost: 0.0000000000000009190438168193556, best_cost: 0.0000000000000009190438168193556, gradient_count: 125, cost_count: 84, gamma: 0.0008377658208246105, time: 0.038537432
Mar 05 15:12:58.024 INFO iter: 41, cost: 0.0000000000000000010640888952026633, best_cost: 0.0000000000000000010640888952026633, gradient_count: 127, cost_count: 85, gamma: 0.0008326080674532384, time: 0.037460313
Mar 05 15:12:58.060 INFO iter: 42, cost: 0.0000000000000000000003834750428076903, best_cost: 0.0000000000000000000003834750428076903, gradient_count: 129, cost_count: 86, time: 0.036246197, gamma: 0.0008348526737634932
OptimizationResult:
    Solver:        L-BFGS
    param (best):  [0.9999999999993618, 0.0999999999999094]
    cost (best):   0.0000000000000000000003834750428076903
    iters (best):  42
    iters (total): 43
    termination:   Solver converged
    time:          7.139444823s

Best parameter vector: [0.9999999999993618, 0.0999999999999094]
True parameter vector: [1.0, 0.1]