De Boor's algorithm

In the mathematical subfield of numerical analysis, de Boor's algorithm is a polynomial-time and numerically stable algorithm for evaluating spline curves in B-spline form. It is a generalization of de Casteljau's algorithm for Bézier curves. The algorithm was devised by German-American mathematician Carl R. de Boor. Simplified, potentially faster variants of the de Boor algorithm have been created but they suffer from comparatively lower stability.

Introduction

A general introduction to B-splines is given in the main article. Here we discuss de Boor's algorithm, an efficient and numerically stable scheme to evaluate a spline curve $\mathbf {S} (x)$ at position $x$. The curve is built from a sum of B-spline functions $B_{i,p}(x)$ multiplied with potentially vector-valued constants $\mathbf {c} _{i}$, called control points, $$\mathbf {S} (x)=\sum _{i}\mathbf {c} _{i}B_{i,p}(x).$$ B-splines of order $p 1$ are connected piece-wise polynomial functions of degree $p$ defined over a grid of knots ${t_{0},\dots ,t_{i},\dots ,t_{m}}$ (we always use zero-based indices in the following). De Boor's algorithm uses O(p²) O(p) operations to evaluate the spline curve. Note: the main article about B-splines and the classic publications use a different notation: the B-spline is indexed as $B_{i,n}(x)$ with $n=p 1$.

Local support

B-splines have local support, meaning that the polynomials are positive only in a finite domain and zero elsewhere. The Cox-de Boor recursion formula shows this: $$B_{i,0}(x):={\begin{cases}1&{\text{if }}\quad t_{i}\leq x<t_{i 1}\\0&{\text{otherwise}}\end{cases}}$$ $$B_{i,p}(x):={\frac {x-t_{i}}{t_{i p}-t_{i}}}B_{i,p-1}(x) {\frac {t_{i p 1}-x}{t_{i p 1}-t_{i 1}}}B_{i 1,p-1}(x).$$

Let the index $k$ define the knot interval that contains the position, $x\in [t_{k},t_{k 1})$. We can see in the recursion formula that only B-splines with $i=k-p,\dots ,k$ are non-zero for this knot interval. Thus, the sum is reduced to: $$\mathbf {S} (x)=\sum _{i=k-p}^{k}\mathbf {c} _{i}B_{i,p}(x).$$

It follows from $i\geq 0$ that $k\geq p$. Similarly, we see in the recursion that the highest queried knot location is at index $k 1 p$. This means that any knot interval $[t_{k},t_{k 1})$ which is actually used must have at least $p$ additional knots before and after. In a computer program, this is typically achieved by repeating the first and last used knot location $p$ times. For example, for $p=3$ and real knot locations $(0,1,2)$, one would pad the knot vector to $(0,0,0,0,1,2,2,2,2)$.

The algorithm

With these definitions, we can now describe de Boor's algorithm. The algorithm does not compute the B-spline functions $B_{i,p}(x)$ directly. Instead it evaluates $\mathbf {S} (x)$ through an equivalent recursion formula.

Let $\mathbf {d} _{i,r}$ be new control points with $\mathbf {d} _{i,0}:=\mathbf {c} _{i}$ for $i=k-p,\dots ,k$. For $r=1,\dots ,p$ the following recursion is applied: $$\mathbf {d} _{i,r}=(1-\alpha _{i,r})\mathbf {d} _{i-1,r-1} \alpha _{i,r}\mathbf {d} _{i,r-1};\quad i=k-p r,\dots ,k$$ $$\alpha _{i,r}={\frac {x-t_{i}}{t_{i 1 p-r}-t_{i}}}.$$

Once the iterations are complete, we have $\mathbf {S} (x)=\mathbf {d} _{k,p}$, meaning that $\mathbf {d} _{k,p}$ is the desired result.

De Boor's algorithm is more efficient than an explicit calculation of B-splines $B_{i,p}(x)$ with the Cox-de Boor recursion formula, because it does not compute terms which are guaranteed to be multiplied by zero.

Optimizations

The algorithm above is not optimized for the implementation in a computer. It requires memory for $(p 1) p \dots 1=(p 1)(p 2)/2$ temporary control points $\mathbf {d} _{i,r}$. Each temporary control point is written exactly once and read twice. By reversing the iteration over $i$ (counting down instead of up), we can run the algorithm with memory for only $p 1$ temporary control points, by letting $\mathbf {d} _{i,r}$ reuse the memory for $\mathbf {d} _{i,r-1}$. Similarly, there is only one value of $\alpha$ used in each step, so we can reuse the memory as well.

Furthermore, it is more convenient to use a zero-based index $j=0,\dots ,p$ for the temporary control points. The relation to the previous index is $i=j k-p$. Thus we obtain the improved algorithm:

Let $\mathbf {d} _{j}:=\mathbf {c} _{j k-p}$ for $j=0,\dots ,p$. Iterate for $r=1,\dots ,p$: $$\mathbf {d} _{j}:=(1-\alpha _{j})\mathbf {d} _{j-1} \alpha _{j}\mathbf {d} _{j};\quad j=p,\dots ,r\quad$$ $$\alpha _{j}:={\frac {x-t_{j k-p}}{t_{j 1 k-r}-t_{j k-p}}}.$$ Note that j must be counted down. After the iterations are complete, the result is $\mathbf {S} (x)=\mathbf {d} _{p}$.

Example implementation

The following code in the Python programming language is a naive implementation of the optimized algorithm.

def deBoor(k: int, x: int, t, c, p: int):
    """Evaluates S(x).

    Arguments
    ---------
    k: Index of knot interval that contains x.
    x: Position.
    t: Array of knot positions, needs to be padded as described above.
    c: Array of control points.
    p: Degree of B-spline.
    """
    d = [c[j   k - p] for j in range(0, p   1)] 

    for r in range(1, p   1):
        for j in range(p, r - 1, -1):
            alpha = (x - t[j   k - p]) / (t[j   1   k - r] - t[j   k - p]) 
            d[j] = (1.0 - alpha) * d[j - 1]   alpha * d[j]

    return d[p]

See also

• De Casteljau's algorithm
• Bézier curve
• Non-uniform rational B-spline

External links

• De Boor's Algorithm
• The DeBoor-Cox Calculation

Computer code

• PPPACK: contains many spline algorithms in Fortran
• GNU Scientific Library: C-library, contains a sub-library for splines ported from PPPACK
• SciPy: Python-library, contains a sub-library scipy.interpolate with spline functions based on FITPACK
• TinySpline: C-library for splines with a C wrapper and bindings for C#, Java, Lua, PHP, Python, and Ruby
• Einspline: C-library for splines in 1, 2, and 3 dimensions with Fortran wrappers

References

Works cited

• Carl de Boor (2003). A Practical Guide to Splines, Revised Edition. Springer-Verlag. ISBN 0-387-95366-3.