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Shape preserving piecewise polynomial interpolation — Examples
1 · Prerequisites
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Countability and Uncountability
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Limits of Real Functions
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Outward rounded interval and ball arithmetic
- Relations, Functions, and Quotients
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Shape preserving piecewise polynomial interpolation
- Splines and Series Methods
- Suprema and Infima
- The Derivative and the Mean Value Theorems
- The ZFC Axioms and the Basic Set Constructions
- Topology of ℝ
2 · Summary
Overview
The examples compute a natural cubic spline that dips below nonnegative table values, a PCHIP interpolant that retains a flat monotone segment, and a smooth-extremum witness where the PCHIP value error is only of second order. A Bernstein quadratic also shows why positive endpoint samples do not guarantee a positive interpolant. Each calculation uses exact formulas and keeps the hypotheses of the A-page results visible.
3 · Logical flowchart
4 · Definitions, theorems and proofs
None yet.
5 · Examples, counterexamples and false statements
Positive endpoint samples do not ensure a positive interpolant
Example
The quadratic is positive at both sampled endpoints but negative at . Its degree-two Bernstein controls are ; the negative middle control explains why the all-controls-nonnegative hypothesis in Bernstein control values bound a piecewise polynomial does not apply.
Facts & Assumptions
Given: The interval , the polynomial , and exact real arithmetic.
Piecewise Bernstein representation: a degree-two Bernstein polynomial has the form .
Bernstein control values bound a piecewise polynomial: nonnegative Bernstein controls suffice for nonnegativity throughout the interval.
The set of -element subsets and the binomial coefficient : for every natural .
The canonical natural of a field: the canonical embedding sends zero to zero and each successor to the previous image plus one.
Verification
By [F3], ; applying the recursion in [F4] twice shows its canonical real value is . Expanding gives , so [F1] identifies its Bernstein controls as , , .
Direct substitution gives , while . Thus positive samples at the two endpoints do not control the interpolant between them.
The middle Bernstein control is negative, so the sufficient hypothesis in [F2] is absent; the negative value in step 1.2 is consistent with the control-hull theorem.
A natural cubic spline undershoots a nonnegative table
Example
Consider the nonnegative table . On the three unit cells, with on cell , define These pieces form a natural cubic spline interpolating the table, but for . PCHIP on the same data has a flat middle segment at zero and remains between the nonnegative cell endpoints.
Facts & Assumptions
Given: The four tabulated nodes and values, the displayed piecewise cubic , and exact real arithmetic.
Natural cubic splines: a natural cubic spline is , piecewise cubic, and has second derivative zero at its first and last knots.
PCHIP slope selection: PCHIP selects zero interior slope when adjacent secants have nonpositive product and uses the Hermite pieces from the resulting nodal slopes.
PCHIP is C1, monotone on each data segment, and has no interval overshoot: every PCHIP cell is monotone between its endpoint values; a flat cell is constant.
Sum, product, quotient, and chain rules from difference quotients: the power rule holds for every integer .
Verification
Each displayed piece has endpoint values , , , , and , so the pieces interpolate the given table. By [F4], their first derivatives are , , and ; the derivative values match at both shared knots. Their second derivatives are , , and ; these match at shared knots, and . By [F1], this is a natural cubic spline.
For , , although every tabulated value is nonnegative. Thus the natural cubic spline undershoots the data range on the middle cell.
The secants are , , and . By [F2], the interior slopes at nodes and are both zero because each neighboring pair includes a zero secant; the Hermite piece joining the two zero values with zero slopes is constant zero. By [F3], the first and last PCHIP pieces remain between and , and between and , respectively. Hence PCHIP is nonnegative throughout the interval and does not reproduce the natural spline's undershoot.
PCHIP preserves a flat part of monotone data
Example
For nodes and values , the PCHIP data are monotone with one flat interval. The algorithm selects slopes , and the interpolant is constant on while remaining monotone on the adjoining cells.
Facts & Assumptions
Given: The nodes , values , and exact real arithmetic.
PCHIP slope selection: the secant slopes determine the zero, harmonic-mean, and one-sided endpoint rules, and the output pieces are cubic Hermite pieces.
PCHIP is C1, monotone on each data segment, and has no interval overshoot: each segment is monotone between its data endpoints, and a zero-secant segment is constant.
Verification
The secants are , , and . The interior rule in [F1] gives . The endpoint rule gives and, by reflection, ; neither clip nor cap changes these values.
On the middle cell both endpoint values are and both slopes are zero, so the Hermite formula is the constant polynomial . On the first cell it is , and on the last cell it is , for normalized local coordinate .
The shape theorem [F2] makes the first and last pieces monotone from to and from to and makes the middle piece constant. Thus the interpolant is globally nondecreasing, has no overshoot, and retains the flat interval exactly.
PCHIP loses third-order value accuracy next to a smooth extremum
Example
Fix on . For each integer , use the uniform grid , , with . The function is smooth and has a strict extremum at zero. On the cell , PCHIP's local maximum error is at least , so no uniform value-error bound on this fixed interval can hold across the extremum.
Facts & Assumptions
Given: An integer , , on , the uniform nodes for , and exact real arithmetic.
PCHIP slope selection: opposite-sign neighboring secants give zero interior slope; same-sign secants use the weighted harmonic mean.
Piecewise cubic Hermite interpolant: the segment is formed from endpoint values and slopes using .
PCHIP has third-order value accuracy on uniform nonstationary grids: the third-order value bound assumes a uniform grid and a derivative bounded away from zero on the whole interval.
Sum, product, quotient, and chain rules from difference quotients: the power rule holds for every integer .
Heine-Borel by bisection: every closed bounded interval is compact: every closed interval with is compact.
Extreme value theorem: a continuous real function on a nonempty compact subset of attains a greatest and a least value: a continuous real function on a nonempty compact subset of attains its maximum.
Verification
The secants on , , , and are , , , and . At , the equal-width weights are , so [F1] gives . At the adjacent secants have opposite signs, so [F1] gives .
With on , [F2] gives , whereas . Therefore .
On this cell, step 2.1 gives the error , since . This expression is a polynomial in through , so [F4] makes it differentiable, hence continuous, on the cell. As , the interval is nonempty and compact by [F5]; [F6] therefore gives an attained maximum of there. Since corresponds to a point of the cell and step 2.1 gives at that point, the maximum is at least . Any uniform bound on these grids with independent of would imply for all sufficiently large , which is impossible as . The hypothesis in [F3] also fails because [F4] gives .