Müntz–Szász Theorem

Can we skip some powers and still approximate everything?

The test is the sum of the reciprocals of the exponents. If that sum grows forever, the ingredients are dense enough. If it stays finite, some function remains forever out of reach. The scene fits the square-root function by least squares and compares a dense set with a sparse one.

For example, think of measuring with a 📏 ruler. Marks packed endlessly close let you measure any length well. Marks that thin out like 1, 4, 9 leave some lengths unmeasurable forever.

span⁡{1,xλ1,xλ2,…} dense in C[0,1]  ⟺  ∑n1λn=∞\operatorname{span}\{1, x^{\lambda_1}, x^{\lambda_2}, \ldots\} \text{ dense in } C[0,1] \iff \sum_{n} \frac{1}{\lambda_n} = \infty
N=4,6,8:∥pdense−⋅ ∥∞=0.1003→0.0708→0.0544N = 4, 6, 8: \|p_{\mathrm{dense}} - \sqrt{\cdot}\,\|_\infty = 0.1003 \to 0.0708 \to 0.0544

Examples & Exercises

Prove ∑n≥11n2=π26<∞\sum_{n \ge 1} \frac{1}{n^2} = \frac{\pi^2}{6} < \infty, and deduce from the necessity direction of the Müntz–Szász theorem that span⁡{1,x,x4,x9,…}\operatorname{span}\{1, x, x^4, x^9, \ldots\} is not dense in C[0,1]C[0,1].

The proof of the necessity direction itself is marked as an idea-level sketch.

∑k=1∞bk2=4∑k=1∞1k2=1π∫−ππx2 dx=2π23,bk=2(−1)k+1k\sum_{k=1}^{\infty} b_k^2 = 4\sum_{k=1}^{\infty} \frac{1}{k^2} = \frac{1}{\pi}\int_{-\pi}^{\pi} x^2\,dx = \frac{2\pi^2}{3}, \qquad b_k = \frac{2(-1)^{k+1}}{k}
∑n=1∞1n2=π26<∞  ⇒  span⁡{1,xn2} not dense\sum_{n=1}^{\infty} \frac{1}{n^2} = \frac{\pi^2}{6} < \infty \;\Rightarrow\; \operatorname{span}\{1, x^{n^2}\} \text{ not dense}
N=6N = 6: the sparse-basis error ≈0.1676\approx 0.1676 stays stuck near 0.160.16 — the proof below is why this stall was inevitable.
Show proof
  1. First compute ∑1/n2=π2/6\sum 1/n^2 = \pi^2/6 (complete proof). Take f(x)=xf(x) = x on (−π,π)(-\pi, \pi). Since ff is continuous, f∈L2(−π,π)f \in L^2(-\pi, \pi) (indeed ∫−ππx2 dx<∞\int_{-\pi}^{\pi} x^2\,dx < \infty), so Parseval's identity applies to ff.

    Since ff is odd, every cosine coefficient vanishes: a0=0a_0 = 0 and ak=0a_k = 0 for all kk, and only the sine coefficients bk=1π∫−ππxsin⁡(kx) dx=2(−1)k+1kb_k = \frac{1}{\pi}\int_{-\pi}^{\pi} x \sin(kx)\,dx = \frac{2(-1)^{k+1}}{k} (integration by parts, next step) survive. Parseval in the form 1π∫−ππf2 dx=a022+∑k=1∞(ak2+bk2)\frac{1}{\pi}\int_{-\pi}^{\pi} f^2\,dx = \frac{a_0^2}{2} + \sum_{k=1}^{\infty} (a_k^2 + b_k^2) therefore reads 1π∫−ππx2 dx=∑k=1∞bk2\frac{1}{\pi}\int_{-\pi}^{\pi} x^2\,dx = \sum_{k=1}^{\infty} b_k^2. The left side is 2π23\frac{2\pi^2}{3}, and bk2=4k2b_k^2 = \frac{4}{k^2}, so 4∑k=1∞1k2=2π234\sum_{k=1}^{\infty} \frac{1}{k^2} = \frac{2\pi^2}{3}, i.e. ∑k=1∞1k2=π26≈1.6449<∞\sum_{k=1}^{\infty} \frac{1}{k^2} = \frac{\pi^2}{6} \approx 1.6449 < \infty.

  2. Compute the coefficient bkb_k by explicit integration by parts. Since xsin⁡(kx)x\sin(kx) is even, bk=1π∫−ππxsin⁡(kx) dx=2π∫0πxsin⁡(kx) dxb_k = \frac{1}{\pi}\int_{-\pi}^{\pi} x\sin(kx)\,dx = \frac{2}{\pi}\int_{0}^{\pi} x\sin(kx)\,dx. With u=xu = x, dv=sin⁡(kx) dxdv = \sin(kx)\,dx we get du=dxdu = dx, v=−cos⁡(kx)kv = -\frac{\cos(kx)}{k}, so the antiderivative is ∫xsin⁡(kx) dx=−xcos⁡(kx)k+∫cos⁡(kx)k dx=−xcos⁡(kx)k+sin⁡(kx)k2\int x\sin(kx)\,dx = -\frac{x\cos(kx)}{k} + \int \frac{\cos(kx)}{k}\,dx = -\frac{x\cos(kx)}{k} + \frac{\sin(kx)}{k^2}.

    Evaluating from 00 to π\pi gives [−πcos⁡(kπ)k+sin⁡(kπ)k2]−0=−π(−1)kk\left[-\frac{\pi\cos(k\pi)}{k} + \frac{\sin(k\pi)}{k^2}\right] - 0 = -\frac{\pi(-1)^k}{k} (since sin⁡(kπ)=0\sin(k\pi) = 0). Hence bk=2π⋅(−π(−1)kk)=−2(−1)kk=2(−1)k+1kb_k = \frac{2}{\pi}\cdot\left(-\frac{\pi(-1)^k}{k}\right) = -\frac{2(-1)^k}{k} = \frac{2(-1)^{k+1}}{k}.

  3. Now quote the necessity direction of the Müntz–Szász theorem: if ∑1λn<∞\sum \frac{1}{\lambda_n} < \infty then span⁡{1,xλ1,xλ2,…}\operatorname{span}\{1, x^{\lambda_1}, x^{\lambda_2}, \ldots\} is not dense in C[0,1]C[0,1]. [Sketch marker: the full proof of this direction needs complex analysis — convergence of ∑1/λn\sum 1/\lambda_n makes a Blaschke-type infinite product converge, producing a nonzero analytic function orthogonal (in a weighted L2L^2 inner product) to every xλnx^{\lambda_n}; Hahn–Banach then separates a continuous function at fixed positive distance from the span. We use only the conclusion here.]

    For λn=n2\lambda_n = n^2 we have ∑1λn=π26<∞\sum \frac{1}{\lambda_n} = \frac{\pi^2}{6} < \infty, so span⁡{1,x,x4,x9,…}\operatorname{span}\{1, x, x^4, x^9, \ldots\} is not dense in C[0,1]C[0,1] — which is why the sparse basis in the scene stays stuck at error ≈0.16\approx 0.16.

Prove that every uniform limit of even polynomials on [−1,1][-1, 1] is an even function (complete proof), and conclude that span⁡{1,x2,x4,…}\operatorname{span}\{1, x^2, x^4, \ldots\} is not dense in C[−1,1]C[-1, 1] — a warm-up done directly, without quoting Müntz–Szász.

f(x)=lim⁡n→∞pn(x)=lim⁡n→∞pn(−x)=f(−x)f(x) = \lim_{n \to \infty} p_n(x) = \lim_{n \to \infty} p_n(-x) = f(-x)
g(x)=x∈C[−1,1] is not even  ⇒  span⁡{x2k} not denseg(x) = x \in C[-1,1] \text{ is not even} \;\Rightarrow\; \operatorname{span}\{x^{2k}\} \text{ not dense}
N=6N = 6: the dense basis closes in on x\sqrt{x} to error ≈0.0708\approx 0.0708 — with all polynomials available (unlike even degrees only), every continuous function is reachable.
Show proof
  1. Let pnp_n be even polynomials with pn→fp_n \to f uniformly on [−1,1][-1, 1]. Uniform convergence implies pointwise convergence, so f(x)=lim⁡n→∞pn(x)f(x) = \lim_{n \to \infty} p_n(x) for each x∈[−1,1]x \in [-1, 1]. Each pnp_n is even, so pn(x)=pn(−x)p_n(x) = p_n(-x), and hence f(x)=lim⁡n→∞pn(x)=lim⁡n→∞pn(−x)=f(−x)f(x) = \lim_{n \to \infty} p_n(x) = \lim_{n \to \infty} p_n(-x) = f(-x).

    So ff is even — a uniform limit of even polynomials is always even. (End of proof.)

  2. g(x)=xg(x) = x is continuous on [−1,1][-1, 1] but not even. If span⁡{1,x2,x4,…}\operatorname{span}\{1, x^2, x^4, \ldots\} were dense in C[−1,1]C[-1, 1], some sequence of even polynomials would converge uniformly to gg, and by what we just proved the limit gg would have to be even — a contradiction. Hence span⁡{1,x2,x4,…}\operatorname{span}\{1, x^2, x^4, \ldots\} is not dense in C[−1,1]C[-1, 1].

    (Note: this is a C[−1,1]C[-1,1] statement, distinct from the scene's sparse basis {1,x,x4,…}\{1, x, x^4, \ldots\} on C[0,1]C[0,1] — the non-density of the latter is the job of the Müntz–Szász necessity direction in the first problem.)

For λn=n\lambda_n = n, show ∑1n=∞\sum \frac{1}{n} = \infty and prove directly — via the Weierstrass approximation theorem, without quoting the sufficiency direction of Müntz–Szász — that span⁡{1,x,x2,…}\operatorname{span}\{1, x, x^2, \ldots\} is dense in C[0,1]C[0,1].

Concretely, check x2∈span⁡{1,x,…,xN}x^2 \in \operatorname{span}\{1, x, \ldots, x^N\} for N≥2N \ge 2, and read off the numbers with which the scene's dense basis closes in on x\sqrt{x}.

Hn=∑k=1n1k>∫1n+1dxx=ln⁡(n+1)→∞H_n = \sum_{k=1}^{n} \frac{1}{k} > \int_1^{n+1} \frac{dx}{x} = \ln(n+1) \to \infty
span⁡{1,x,x2,…}=all polynomials\operatorname{span}\{1, x, x^2, \ldots\} = \text{all polynomials}
all polynomials→Weierstrassdense in C[0,1]\text{all polynomials} \xrightarrow{\text{Weierstrass}} \text{dense in } C[0,1]
N=8N = 8: dense-basis error ≈0.0544\approx 0.0544 — down from 0.10030.1003 at N=4N = 4 and 0.07080.0708 at N=6N = 6. A basis with ∑1n=∞\sum \frac{1}{n} = \infty never stalls.
Show proof
  1. The harmonic series diverges: for Hn=∑k=1n1kH_n = \sum_{k=1}^{n} \frac{1}{k}, each term satisfies 1k≥∫kk+1dxx\frac{1}{k} \ge \int_{k}^{k+1} \frac{dx}{x}, so Hn≥∫1n+1dxx=ln⁡(n+1)→∞H_n \ge \int_{1}^{n+1} \frac{dx}{x} = \ln(n+1) \to \infty. Hence for λn=n\lambda_n = n we have ∑1λn=∞\sum \frac{1}{\lambda_n} = \infty.

    (Numerical witness: H1000≈7.4855>ln⁡(1001)≈6.9088H_{1000} \approx 7.4855 > \ln(1001) \approx 6.9088.)

  2. For λn=n\lambda_n = n, span⁡{1,x,x2,…}\operatorname{span}\{1, x, x^2, \ldots\} is exactly the set of all (real-coefficient) polynomials — xkx^k is itself the kk-th basis element, so every polynomial of degree at most dd is a linear combination of the first d+1d+1 basis elements. By the Weierstrass approximation theorem, polynomials are uniformly dense in C[0,1]C[0,1], so this span is dense too.

    No need to quote the sufficiency direction of Müntz–Szász at all — Weierstrass handles this case directly.

  3. Concrete check: for N≥2N \ge 2, x2x^2 is itself a basis element, so it trivially lies in span⁡{1,x,…,xN}\operatorname{span}\{1, x, \ldots, x^N\} — exact membership, not approximation. The scene shows this density working in practice even on a function with a cusp like x\sqrt{x}: the dense basis's least-squares error shrinks 0.1003→0.0708→0.05440.1003 \to 0.0708 \to 0.0544 for N=4,6,8N = 4, 6, 8.

    (End of proof.)

6. Approximation