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The first-kind Chebyshev polynomial $T_n$ converts a multiple-angle cosine into a polynomial:The cosine addition formula gives the recurrence relationThis constructs polynomials valid for every real $x$, including outside $[-1,1]$, where the real-angle description is unavailable. For example,For $n\ge 1$, $T_n$ has polynomial degree $n$ and leading coefficient $2^{n-1}$. On $[-1,1]$ its uniform norm is one, even as its oscillations become more numerous.^[ref][ref]

The polynomial roots of $T_n$ are especially simple:They correspond to equally spaced angles, but their locations in $x$ cluster near $-1$ and $1$. For $T_3$, the polynomial roots are $\sqrt{3}/2,0,-\sqrt{3}/2$. For $T_8$, the positive polynomial roots are approximately $0.980785,0.831470,0.555570,0.195090$, with their negatives completing the set. The distance from the right endpoint to the nearest polynomial root is $1-\cos (\pi /(2n))\sim {\pi }^2/(8n^2)$; central neighboring polynomial roots are separated on the larger scale $1/n$. The small change of cosine near angles zero and $\pi$ explains the clustering.

These polynomial roots supply $n$ interpolation nodes for a polynomial interpolation of polynomial degree at most $n-1$. They differ from the $n+1$ Chebyshev-Lobatto nodes $\cos (j\pi /n)$, which include both endpoints. This choice of polynomial roots has nodal polynomialThe monic Chebyshev extremal polynomial minimizes the uniform norm of this product among monic polynomials of polynomial degree $n$ on $[-1,1]$. Consequently, if $f$ has $n$ continuous derivatives, the usual polynomial interpolation error gives the useful boundThis optimizes the nodal factor in that bound, not the actual error for every function. Endpoint clustering helps control the large endpoint oscillations of equally spaced interpolation associated with Runge's phenomenon. The two node conventions and their geometric construction are discussed in Trefethen's authored text.^[ref]

The same cosine substitution for polynomial approximation explains the weighted orthogonality:The endpoint singularities of the weight are integrable. Thus the Chebyshev polynomials are orthogonal polynomials for this weight, making coefficients in a least-squares polynomial in an orthogonal-polynomial basis separate cleanly. Their polynomial roots also give Chebyshev–Gauss quadrature, connecting polynomial interpolation to Gaussian quadrature.^[ref]

Accuracy still depends on the target function. The absolute value function $|x|$ has a corner at zero, so the hypothesis requiring continuous derivatives in the displayed polynomial interpolation error bound fails. Endpoint clustering cannot remove that corner. A jump poses a stronger obstruction: polynomials are continuous functions, so they cannot converge in the uniform norm to a discontinuous function on the whole interval. Chebfun's documented examples show persistent overshoot for such jumps; these are a limitation of global polynomial interpolation, not a universal promise of rapid accuracy from Chebyshev polynomials.^[ref]

The first Jackson theorem for periodic approximation applied to $|\sin \theta |$ gives algebraic best error $O(n^{-1})$ for $\sqrt{1-x^2}$. Its ordinary interval modulus of continuity at $1/n$ is at least $\sqrt{2/n-1/n^2}$. Substituting that error rate into the unmodified inverse theorem for trigonometric approximation would instead bound the modulus by $O(\log (n+1)/n)$, a contradiction. Algebraic inverse estimates must incorporate the endpoint compression of cosine substitution for polynomial approximation.

Every continuous $2\pi$-periodic function admits degree-at-most-$n$ trigonometric polynomials with supremum norm error bounded by a universal constant times its modulus of continuity at scale $1/n$, for $n\ge 1$. This first-order Jackson-type estimate transfers to algebraic polynomials through cosine substitution for polynomial approximation.

For a function on an interval, the modulus of continuity is $\omega (f,t)={\sup }_{|x-y|\le t}|f(x)-f(y)|$, with the pair restricted to that interval. For a periodic function the equivalent definition is ${\sup }_{\theta ,|h|\le t}|g(\theta +h)-g(\theta )|$. The mean value theorem and $|\sin \theta |\le 1$ giveBoth cosine values lie in $[-1,1]$. Hence for $|h|\le t$,Taking the supremum provesIf the periodic modulus of continuity is defined using circular distance instead, choose lifts at that shortest distance and apply the same argument. The cosine substitution for polynomial approximation therefore does not enlarge this modulus.

The first Jackson theorem for periodic approximation states that an absolute constant $C_J$ exists such that, for every continuous $2\pi$-periodic function $g$ and integer $n\ge 1$, there is a trigonometric polynomial $q_n$ of degree at most $n$ satisfyingApply it to the continuous periodic function $g(\theta )=f(\cos \theta )$, which is even. Replace the approximant by its even partSince $g(-\theta )=g(\theta )$, the triangle inequality gives $\Vert g-q_n^{\mathrm{ev}}{\Vert }_{\infty }\le \Vert g-q_n{\Vert }_{\infty }$. This symmetrization cancels all sine terms, so $q_n^{\mathrm{ev}}={\sum }_{j=0}^n{\alpha }_j\cos (j\theta )$. The Chebyshev polynomials satisfy $T_j(\cos \theta )=\cos (j\theta )$ and have degree $j$; the latter follows inductively from $T_0=1$, $T_1=x$ and $T_{j+1}=2x{T}_j-T_{j-1}$. Thereforeis an algebraic polynomial of degree at most $n$, and $p_n(\cos \theta )=q_n^{\mathrm{ev}}(\theta )$. Because cosine maps onto $[-1,1]$, the supremum norms agree exactly:Finally, the definition of the best error, periodic Jackson approximation, and part (a) give the successive boundsThusNeither the constant nor the choice of approximation space depends on $f$. The cosine substitution for polynomial approximation and even symmetrization are what preserve the required degree bound.

For a periodic function use the modulus of continuityOn the interval, take the supremum over pairs of points whose distance is at most $\delta$. The mean value theorem applied to cosine, whose derivative has absolute value at most one, givesBoth cosine values lie in $[-1,1]$, so for $|h|\le \delta$,Taking the supremum provesThe cosine substitution for polynomial approximation is also a linear isometry in the supremum norm, since cosine maps a full period onto $[-1,1]$; its image consists of even continuous periodic functions.