ConceptPythonBeginner
numpy.linalg.cond
Python. Called in 3 tutorials; each line below leads to its place in the tutorial.
ConceptPythonIntermediate
Rational approximation: why a resonance needs a ratio of polynomials
ConceptPythonIntermediate
The condition number: how many digits a linear solve can lose
- print(f"κ(A) = {np.linalg.cond(A):.1f}, log10 κ = {np.log10(np.linalg.cond(A)):.2f}")
- print(f"eps = {eps:.2g}, κ eps = {np.linalg.cond(A) * eps:.2g}, digits kept about {-np.log10(np.linalg.cond(A) * eps):.1f}")
- print(f"0.001 I₁₀: det = {np.linalg.det(S):.3g}, κ = {np.linalg.cond(S):.1f}")
- kappa.append(np.linalg.cond(H))
- kappa1 = np.linalg.cond(H, 1)
- print(f"polynomial fit, degree {deg:2d}: κ(X) = {np.linalg.cond(X):.3g}, κ(XᵀX) = {np.linalg.cond(X.T @ X):.2g}")
and 4 more lines in this tutorial