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Under the finite-alphabet, positive-reference, closed-convex set assumptions above, every feasible law $r\in \mathcal{C}$ satisfiesThus a feasible law's excess relative entropy above the optimum is at least its relative entropy from the optimizer. The information projection supplies a quantitative comparison with every other feasible law, not merely the location of a minimum.^[ref]

For affine expected value constraints, the result becomes a Pythagorean identity for relative entropy. In relative entropy minimization under a mean constraint, the logarithm of $q_i^{\ast }/p_i$ is an affine function of the outcome $a_i$. Any feasible $r$ has the same normalization and mean as $q^{\ast }$, making the residual in the three-point identity for relative entropy zero.^[ref]

The recession cone of a nonempty convex set $C$ consists of directions $d$ for which $x+td\in C$ for every $x\in C$ and $t\ge 0$. For a linear polyhedron $P=\{x:Ax\le b\}$ it is exactly $\{d:Ad\le 0\}$. A nonzero recession direction gives an unbounded ray, so a bounded nonempty polyhedron has recession cone $\{0\}$. This excludes spurious zero-scale feasible points in the Charnes-Cooper transformation.

Let $K$ be a nonempty compact convex set in a Banach space and $T:K\to K$ a continuous map. For every $\epsilon >0$, there is a finite-dimensional linear subspace $L_{\epsilon }$ and a continuous map $T_{\epsilon }:K\to K\cap L_{\epsilon }$ that approximates $T$ uniformly to within $\epsilon$ in norm.

Choose $y_1,\dots ,y_N\in T(K)$ whose open radius-$\epsilon$ balls cover the compact image $T(K)$. Set $w_i(x)=\max \{0,\epsilon -\Vert Tx-y_i\Vert \}$ andThe denominator is positive. This convex combination belongs to $K\cap \mathrm{span}\{y_1,\dots ,y_N\}$ and depends continuously on $x$. Every contributing $y_i$ has distance less than $\epsilon$ from $Tx$, so the triangle inequality gives $\Vert T_{\epsilon }(x)-Tx\Vert <\epsilon$.

Every continuous map from a nonempty compact convex set $K$ in a Banach space to itself has a fixed point. A useful equivalent form takes a nonempty closed bounded convex set $C$ and a continuous map $T:C\to C$ whose image is relatively compact. To obtain this version from the compact-domain version, restrict $T$ to $\overline{\mathrm{conv}}T(C)\subset C$ and use closed convex hull of a compact set.

For a proof on $K$, the Schauder approximation lemma gives continuous maps $T_{\epsilon }:K\to K\cap L_{\epsilon }$ with finite-dimensional $L_{\epsilon }$ and $\Vert T-T_{\epsilon }{\Vert }_{\infty }\le \epsilon$. The Brouwer fixed-point theorem on the nonempty compact convex set $K\cap L_{\epsilon }$ gives $x_{\epsilon }=T_{\epsilon }{x}_{\epsilon }$, so $\Vert T{x}_{\epsilon }-x_{\epsilon }\Vert \le \epsilon$. A convergent subsequence as $\epsilon \downarrow 0$ and the continuity of $T$ give an exact fixed point.

If a convex set $E$ in a real normed vector space contains $B(0,ϵ)$, every $x\notin E$ admits a continuous linear functional $T$ with $Tx\ge 1\ge Te$ for all $e\in E$, even if $E$ is not closed. Its Minkowski functional $p(v)=\inf \{t>0:v\in tE\}$ is sublinear functional, satisfies $p(v)\le \Vert v\Vert /ϵ$, $p(e)\le 1$, and $p(x)\ge 1$. Extend $tx\mapsto tp(x)$ by the dominated Hahn-Banach theorem. The resulting domination $T\le p$ applied to $v$ and $-v$ makes $T$ continuous and supplies the separation inequalities.

A subset $K$ of a normed space is sequentially weakly closed when every weakly convergent sequence in $K$ has its limit in $K$. Every weakly closed set has this property. For a convex set in a Banach space, it is equivalent to closure in the norm topology: Mazur lemma produces norm convergent convex combinations from any weakly convergent sequence, and conversely norm convergence implies weak convergence. For a Hilbert space, the weak Banach–Saks theorem in a Hilbert space supplies these convex combinations as subsequence averages. No equivalence with weak closedness is asserted here for arbitrary nonconvex sets.

Increasing marginal contributions imply $m_i^{\pi }\ge v((S\cap P_i)\cup \{i\})-v(S\cap P_i)$ for $i\in S$. Summing telescopes to ${\sum }_{i\in S}{m}_i^{\pi }\ge v(S)$, while efficiency follows by telescoping over the full ordering. Hence every marginal contribution vector is in the core of a cooperative game. The core is a convex set, so their average, the Shapley value, is in it too. This gives an elementary proof of nonemptiness and stability for a convex cooperative game.

Strict concavity is the displayed property for distinct $x,y$ in a convex set and $0<t<1$. It makes a stationary point of a differentiable function its unique global maximum, whenever such a stationary point exists.

A function $f$ on a convex set is strictly convex whenwhenever $x\ne y$ and $0<t<1$.

For a nonempty closed convex set $C$, the finite-valued support function has subgradients exactly at the maximizing points of $C$. This follows from Fenchel–Young inequality and the conjugacy between the support function and the indicator functional of a constraint set. For compact $C$, the exposed maximizing set is nonempty.

For a convex set $C$ and $x_0\in C$, a supporting hyperplane has a nonzero normal vector $a$ such that $a^{T}x\le a^T{x}_0$ for all $x\in C$. It touches $C$ while containing the set on one side.

A convex set in a finite-dimensional space has a nonzero supporting linear functional at each boundary point. If zero lies outside a nonempty convex set $C$, weak separation gives $p\ne 0$ with $p\cdot c\ge 0$ for all $c\in C$, even when zero belongs to its closure. Strict separation needs stronger hypotheses.

For a differentiable real-valued function on an open convex set, convexity is equivalent to the displayed inequality for all $x,y$ in its domain. To prove the forward direction, convexity gives $[f(x+t(y-x))-f(x)]/t\le f(y)-f(x)$ for $0<t\le 1$. Take the limit as $t\downarrow 0$ using first-order Taylor expansion.

Conversely, put $z=(1-t)x+ty$ and apply the supporting inequality at $z$ to both $x$ and $y$. Multiply the two inequalities by $1-t$ and $t$ and add them. The gradient terms cancel because $(1-t)(x-z)+t(y-z)=0$, leaving $f(z)\le (1-t)f(x)+tf(y)$. This is the defining convexity inequality. The endpoint values $t=0,1$ hold as equalities.

Let $K$ be a convex set in a real Banach space, and let $A:K\to X^{\ast }$ be a strongly monotone operator. If $u,w\in K$ both satisfy the displayed variational inequality, testing with $v=w$ and $v=u$ respectively and adding yields $⟨Au-Aw,u-w⟩\le 0$. Strong monotonicity gives $m\Vert u-w{\Vert }^2\le 0$, hence $u=w$. This proves uniqueness whenever a solution exists.

The upper half-plane is the open setIt contains precisely the complex numbers with positive imaginary part: $1+i$ is inside, $1$ lies on its boundary, and $1-i$ belongs to the lower half-plane. In the complex plane its boundary is the real line; in the Riemann sphere the boundary also includes infinity. It is a convex set, hence a simply connected domain, and the Cayley transform between the half-plane and disk makes it biholomorphic to the unit disc.

With the hyperbolic metric $ds=|dz|/\mathrm{Im}z$, the same domain becomes the Poincaré half-plane model. Its hyperbolic geodesics are vertical lines and upper semicircles whose centres lie on the real line. Under the Cayley transform, these become arcs of circles orthogonal to the unit circle, with diameters as the straight-line cases. ^[ref]

Matching colours identify each hyperbolic geodesic and its image. The vertical line through zero maps to a diameter; the vertical line through one maps to an arc ending at $-i$ and $1$. A semicircle from $-1$ to $1$ also maps to a diameter, whereas the semicircle from $0$ to $2$ maps to a curved arc. Their endpoints lie on the boundary, outside the domains: the endpoints are infinitely far from interior points in hyperbolic distance.

The upper half-plane is useful because its straight boundary and explicit upper half-plane automorphisms make conformal maps and hyperbolic geometry calculable. The picture explains why shortest hyperbolic geodesic routes can look curved in Euclidean coordinates; their lengths are measured with the position-dependent hyperbolic metric.

For historical context, in his letter to Klein of 22 September 1882, Poincaré constructs a quadrilateral bounded by four circles orthogonal to a fundamental circle, and studies transformations generating repeated copies. In modern language, the sides inside that circle are hyperbolic geodesics in a disk model. The letter directly exhibits the connection between transformation groups, geometry and complex functions. ^[ref]

The utility ${\sum }_r{n}_r\log x_r$ sums logarithmic utilities over individual flows. Its first-order condition gives proportional fairness. Its negative is a strictly convex function on active flow rates, giving uniqueness of those rates on a convex set of feasible allocations.