Take and . The Lp space is strictly convex because is strictly convex: for distinct unit functions its pointwise midpoint inequality is strict on a set of positive measure, hence integration gives norm strictly below one. The finite-dimensional linear subspace has a best approximant: any minimizing sequence is bounded by , and finite-dimensional compactness gives a convergent subsequence whose limit minimizes the continuous error. Uniqueness follows from part 1.
Write that unique minimizer as and its residual as . Translation on the circle preserves the Lp norm. Since , the translated polynomial is another minimizer. Uniqueness forces
Also . Therefore the polynomial has residual , with the same norm as , and is again a minimizer. Hence
To extract the consequence, write the trigonometric polynomial as . The first identity gives . Only in the indicated frequency range is a multiple of , so is constant. The second identity makes this constant its own negative and therefore zero. Thus the lower-frequency Lp approximation to a single harmonic is
For the space already consists only of constants, and the negative-translation argument gives the same result.