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For a nonzero vector in a real or complex normed vector space, a norming functional is a continuous linear functional satisfying the displayed conditions. The Hahn-Banach theorem extends the functional sending $av$ to $a\Vert v\Vert$ on its one-dimensional span. Consequently $\Vert w\Vert \ge \mathrm{Re}\ell (w)$, with equality at $v$, giving a supporting linear lower bound for the norm. At zero the zero functional can be used as a supporting functional.

The weak topology $\sigma (E,E^{\ast })$ is the coarsest topology making every continuous linear functional in $E^{\ast }$ continuous. A basic neighborhood of $x$ imposes finitely many inequalities $|f_j(z-x)|<\epsilon$, and a sequence converges weakly precisely when each $f(x_n)$ converges to $f(x)$. The Hahn-Banach theorem makes the dual separate points, so the weak topology is Hausdorff.

Here is a proof of the requested weak subsequence property, the compact-to-sequential direction of the Eberlein-Šmulian theorem. Let $Y$ be the closed linear span of the countable set $\{x_n:n\ge 1\}$. It is a separable Banach space. The Hahn-Banach theorem separates points outside a closed vector subspace from that subspace, so $Y$ is weakly closed in $E$. Therefore $C=K\cap Y$ is weakly compact.

Choose a countable dense set $(u_j)$ in the unit sphere of $Y$, and choose norming functionals $f_j\in Y^{\ast }$ with $\Vert f_j\Vert =1$ and $f_j(u_j)=1$. They separate points: for $y\ne 0$, a sufficiently close $u_j$ to $y/\Vert y\Vert$ gives $|f_j(y)|>0$. This is a countable norming family. Extend the functionals to $E$ by the Hahn-Banach theorem. The evaluation mapis weakly continuous and injective. Its domain is compact and its target Hausdorff, so it is a homeomorphism onto its image: images of closed subsets are compact, hence closed. A countable product of real or complex lines is metrizable, for example by ${\sum }_j{2}^{-j}\min (1,|a_j-b_j|)$. Thus $C$ has a compact metrizable weak topology. Compact metric spaces are sequentially compact, giving a subsequence with $\boxed{x_{n_j}\rightharpoonup x\in C\subseteq K}$. If $Y=\{0\}$ the result is immediate. We did not assert that a separable Banach space has a separable dual or that its weak topology is generally metrizable.

First use independent fair signs ${\epsilon }_i\in \{-1,1\}$, the Rademacher distribution convention for Bernoulli random variables in this setting. Let $\Omega =\{-1,1{\}}^d$ with uniform probability, write $s(\omega )={\sum }_i{\omega }_i{a}_i$ and $F(\omega )=\Vert s(\omega )\Vert$. We prove the sharp Rademacher second-moment inequality by a complete finite-cube calculation.

For a function on this cube define the coordinate-flip generator on a hypercubewhere ${\omega }^{(i)}$ flips just coordinate $i$. The Walsh characters ${\chi }_A(\omega )={\prod }_{i\in A}{\omega }_i$ form an orthonormal basis: distinct characters have expectation-zero product, and there are $2^d$ of them. Flipping a coordinate in $A$ changes its sign and other flips leave it unchanged, so $L{\chi }_A=-|A|{\chi }_A$. Expand the real function $F$ as ${\sum }_A\hat{F}(A){\chi }_A$. Since $F(-\omega )=F(\omega )$, changing variables $\omega \mapsto -\omega$ shows that $\hat{F}(A)=0$ whenever $|A|$ is odd. ThusThis derives the even-function spectral gap on a hypercube, rather than assuming it.

At each vertex with $s(\omega )\ne 0$, choose a norming functional ${\ell }_{\omega }$ of norm one satisfying ${\ell }_{\omega }(s(\omega ))=F(\omega )$, supplied by the Hahn-Banach theorem. For complex $E$ take real parts in the following inequalities. At a vertex with $s(\omega )=0$, use the zero functional. The supporting inequality for the norm givesBut $Ls=-s$, since every coefficient is a degree-one Walsh character. Summing the preceding inequalities with factor $1/2$ therefore proves $LF(\omega )\ge -F(\omega )$. As $F\ge 0$,Rearranging and taking square roots yieldsNo completeness of $E$ is needed. The constant is sharp: with two identical nonzero scalar coefficients, the absolute sum is zero with probability $1/2$ and twice the coefficient magnitude with probability $1/2$.

If Bernoulli instead means fair $\{0,1\}$ variables $b_i$, write $b_i=(1+{\epsilon }_i)/2$ and $a={\sum }_i{a}_i$. Add an independent fair sign ${\epsilon }_0$ and apply the proved estimate to $Y={\epsilon }_{0}a+{\sum }_i{\epsilon }_i{a}_i$. Conditional on either sign of ${\epsilon }_0$, $\Vert Y\Vert$ has the same distribution as $\Vert a+{\sum }_i{\epsilon }_i{a}_i\Vert$, by symmetry of the sign sum. Hence $\Vert Y{\Vert }_{L^p(E)}=2\Vert {\sum }_i{b}_i{a}_i{\Vert }_{L^p(E)}$ for $p=1,2$, proving the same bound in that convention.

The constant is documented in the primary sharp-constant study.

Fairness is essential. For a general $\{0,1\}$ Bernoulli random variable with success probability $0<t<1/2$, a single nonzero scalar coefficient gives $L^2/L^1=1/\sqrt{t}>\sqrt{2}$. Thus the printed term cannot be read as permitting arbitrary Bernoulli probabilities.