[HN Gopher] Notes on Lagrange Interpolating Polynomials
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Notes on Lagrange Interpolating Polynomials
Author : ibobev
Score : 34 points
Date : 2026-03-02 16:01 UTC (7 hours ago)
(HTM) web link (eli.thegreenplace.net)
(TXT) w3m dump (eli.thegreenplace.net)
| wolfi1 wrote:
| the last matrix before the appendix is not the identity matrix,
| right now the matrix is: \begin{bmatrix} 1 & 0 & 0 &
| \dots & 0\\\ 1 & 0 & 0 & \dots & 0\\\ 1 &
| 0 & 0 & \dots & 0\\\ \vdots & \vdots & \vdots
| & \ddots &\vdots \\\ 1 & 0 & 0 & \dots &
| 1 \end{bmatrix}
| eliben wrote:
| Thanks for noticing, I'll fix it shortly
| hdrz wrote:
| The Lagrange polynomials form the normal basis of most Finite
| Elements Method (FEM) software. There are other polynomials which
| are used as well, but these are the workhorse of most solvers.
| commandersaki wrote:
| In the Polynomial Interpolation Theorem, you have r(x) = p(x) -
| r(x), but I think it should be q(x) = p(x) - r(x).
| eliben wrote:
| Fixed, thank you! (it's actually r(x)=p(x)-q(x))
|
| (proof-reading through HN is a mildly embarrassing process,
| sorry about that! I do go over these posts and proof-read them
| several times myself before publishing)
| wenc wrote:
| I used to solve differential algebraic equations using Lagrange
| polynomials.
|
| Essentially you convert the differential equations into an
| algebraic system by discretizing the solution. The method is
| called Orthogonal Collocation on Finite Elements (OCFE), and it
| was developed by chemical engineers.
|
| The Lagrange polynomials were calculated at special knots that
| corresponded to Radau interior points, which work great for stiff
| systems.
|
| It's great for solving differential algebraic equations through
| purely sparse matrix operations, no explicit integration like
| Runge Kutta. (Well, it's implicit Runge Kutta).
| TimorousBestie wrote:
| If you're interested in numerical stability issues associated
| with Lagrange interpolation, Trefethen and Boyd is still
| relevant:
|
| https://people.maths.ox.ac.uk/trefethen/barycentric.pdf
| vector_spaces wrote:
| Every polynomial interpolates itself -- meaning that you can
| often apply this interpolation procedure to your favorite/nemesis
| polynomial or equivalently rewrite your polynomial of interest in
| this Lagrange basis, and see if the coefficients lead you
| anywhere. This is especially helpful in proving polynomial
| inequalities. For instance, Chebyshev polynomials T_n enjoy an
| alternation property over their extremal points -- so in the
| Lagrange basis, in many problems (e.g. Markov type inequalities)
| they emerge as the extremal case in the triangle inequality.
|
| My beef with this approach is that it is a little unsatisfying in
| the sense that it sort of feels like we "got lucky". That is, it
| highlights this special feature (alternation) while burying the
| interesting structure that leads to such polynomials being
| extremal in these problems, as can be seen if you attempt certain
| seemingly trivial extensions of classical inequalities -- but
| nevertheless it's an important trick in extremal polynomial
| theory and approximation more broadly
| 011101101 wrote:
| \begin{pmatrix} H_{1} & H_{2} &\dots & P_{Mc} V_{W} & V_{1}
| &\dots & P_{Mc} \hdotsfor{4} \\\ \end{pmatrix}
| looneysquash wrote:
| Maybe there's a gap in my knowledge of notation, but I was
| confused by:
|
| > Let's define the Lagrange basis functions
|
| It looks like you defined `l_i(x)` as a piecewise function or a
| step function.
|
| But then you show with it's actual definition later in that
| section. (That's what that section is building to and
| explaining.)
| drivebyhooting wrote:
| If we already have access the machinery of the fundamental
| theorem of algebra then the invertibility of the Vandermonde
| Matrix follows as a corollary.
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