{ } Raw JSON

bundles / scipy latest / scipy / integrate / _bvp / estimate_rms_residuals

function

scipy.integrate._bvp:estimate_rms_residuals

source: /scipy/integrate/_bvp.py :528

Signature

def   estimate_rms_residuals ( fun sol x h p r_middle f_middle )

Summary

Estimate rms values of collocation residuals using Lobatto quadrature.

Extended Summary

The residuals are defined as the difference between the derivatives of our solution and rhs of the ODE system. We use relative residuals, i.e., normalized by 1 + np.abs(f). RMS values are computed as sqrt from the normalized integrals of the squared relative residuals over each interval. Integrals are estimated using 5-point Lobatto quadrature [1], we use the fact that residuals at the mesh nodes are identically zero.

In [2] they don't normalize integrals by interval lengths, which gives a higher rate of convergence of the residuals by the factor of h**0.5. I chose to do such normalization for an ease of interpretation of return values as RMS estimates.

Returns

rms_res : ndarray, shape (m - 1,)

Estimated rms values of the relative residuals over each interval.

Aliases

  • scipy.integrate._bvp.estimate_rms_residuals