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A canonical homogeneous inflaton $\varphi (t)$ hasand obeys $\ddot{\varphi }+3H\dot{\varphi }+V^{\prime }(\varphi )=0$.

The positive semidefinite matrices form a convex cone because nonnegative combinations preserve nonnegative quadratic forms.

The convex hull of a set consists of every finite convex combination of its points. It collects all choices obtainable by mixing the original choices with nonnegative weights summing to one. For $S$ in a real vector space,Equivalently this is the smallest convex set containing $S$. Mixing two already mixed points produces another finite convex combination, so the displayed set is convex; every convex set containing $S$ must contain all these mixtures.^[ref]

In an original planar example, take four points $A=(0,0)$, $B=(4,0)$, $C=(3,3)$, $D=(0,2)$ and four additional samples $(1,1)$, $(2,1.7)$, $(3,0.5)$, $(1,0.5)$. Their convex hull is the filled convex polygon $ABCD$, including its boundary. None of the samples is $p=(2,1)$, butThe coefficients are nonnegative and sum to one, providing an explicit membership certificate. Geometrically, first form $U=(3/2,0)=\frac{5}{8}A+\frac{3}{8}B$ on the line segment $AB$, then mix $p=\frac{2}{3}U+\frac{1}{3}C$. The additional interior samples do not enlarge the convex hull.

For a finite nonempty $S\subset {\mathbb{R}}^d$, the convex hull is the image of a probability simplex of weights under the linear map $\lambda \mapsto {\sum }_i{\lambda }_i{s}_i$. The probability simplex is a compact set, and the map is continuous, so this convex hull is compact and closed. A one-point input stays one point; a collinear input stays in a line segment. Filling the convex hull does not necessarily produce a full-dimensional region.

The construction is useful whenever mixtures preserve the intended meaning of a choice. Mixing resource-allocation vectors, for example, interpolates their coordinate requirements. The convex hull describes the resulting feasible region. A coordinate-wise box can admit combinations that no mixture produces, as the outside-point certificate in linear optimization on a convex hull shows.

The second-order cone, also called the Lorentz cone, is

For a nonempty closed convex subset $C$ of a finite-dimensional inner-product space, ${\Pi }_C(x)$ is the unique point of $C$ minimizing $\Vert x-z\Vert$.

This closest-point map is the metric projection onto a closed convex set. Repeated projections onto several sets form the separate projections onto convex sets method.

An extreme point of a convex set is a point that cannot be made by genuinely mixing two different points of the set. For a convex set $C$ in a real vector space, the condition on $p\in C$ isThus $p$ lies on no open line segment between two distinct points of $C$. It is enough to exclude midpoint representations: if $p$ lies inside a line segment, two nearby points on opposite sides have midpoint $p$.^[ref]

The square $[-1,1{]}^2$ has just four extreme points, at $(\pm 1,\pm 1)$. In a convex combination giving a corner, each coordinate is already at its largest or smallest allowed value, forcing both input points to agree with that corner. In contrast, the boundary point $(1,0)$ is not extreme:It is the midpoint of two other points on the same edge. An interior point also admits a small line segment through it.

The unit disk $D=\{z\in {\mathbb{R}}^2:\Vert z{\Vert }_2\le 1\}$ has a different shape: every point on its unit circle is extreme. For a unit vector $p$, the Cauchy-Schwarz inequality givesand equality is possible only at $z=p$. If a convex combination equals $p$, applying this linear functional forces both ingredients to attain that equality. Both must therefore be $p$. Extremality need not look like a sharp corner.

These points help describe all allowable mixtures. Every $(x,y)\in [-1,1{]}^2$ is a convex combination of its four corners, with the weight on $(a,b)\in \{-1,1{\}}^2$ equal to $(1+ax)(1+by)/4$. For the unit disk, write $z=rp$, with $0\le r\le 1$ and $p$ a unit vector; at zero choose any $p$. ThenTwo extreme points suffice for that mixture. The Krein-Milman theorem gives a much broader reconstruction: a nonempty compact convex set in a locally convex space whose topology is Hausdorff is the closed convex hull of its extreme points.

Mark Krein and David Milman published their reconstruction theorem in 1940. Milman's colleagues describe that result and its subsequent use as a method of representing convex sets through their extreme points.^[ref]

A convex polytope is a bounded intersection of finitely many closed half-spaces, equivalently the convex hull of finitely many points.

The line segment joining vectors $x$ and $y$ is

A closed half-space is a set of the form $\{x:a^{T}x\le b\}$ for a nonzero vector $a$; it is a convex set and is closed.

This is the non-strict version of a half-space; replacing the inequality by a strict one gives the open version.

A convex combination is a finite weighted average of points in a real vector space. For $n\ge 1$, it has the formThe nonnegative coefficients forbid subtracting a component, and their unit sum keeps the result on the same affine scale as the inputs. With two points it parametrizes their line segment; equal coefficients give their centroid. A convex set contains every finite convex combination of its points. Conversely, the two-point case already gives the defining property of a convex set.^[ref]

The distinction from a linear combination is concrete. Combining $(0,0)$ and $(2,0)$ with coefficients $-1$ and $2$ gives $(4,0)$, outside their line segment, despite the unit coefficient sum. Using two coefficients both equal to one gives $(2,0)$ here, but their sum is two, so it is not a convex combination with those coefficients. A numerical output may happen to coincide with a point admitting a different convex representation; the definition applies to the specified coefficients.

The nonnegative orthant consists of vectors whose coordinates are all nonnegative. Its interior is the positive orthant, where every coordinate is positive.

It is the closed orthant obtained by choosing the nonnegative sign for every coordinate.

For a convex set $C$ and a differentiable convex objective $F$, projected gradient descent uses

This is the proximal gradient method when the nonsmooth term is the indicator of the feasible convex set, whose proximal operator is the metric projection.

For a real matrix $P$, exactly one of the following holds: there is $\varphi$ with $P\varphi \ge 0$ and $P\varphi \ne 0$, or there is $q>0$ with $P^{T}q=0$. Separating $\mathrm{Im}P$ from the standard simplex proves the result; $q$ may then be normalized to sum to one.

This is an alternative related to Farkas' lemma. It is not the strict-primal Gordan alternative: for the column matrix $(1,0{)}^T$, a nonnegative nonzero primal vector exists, while a componentwise strictly positive one does not.

For a convex function $f$, the subdifferential at $x$ isThe point $x$ minimizes $f$ exactly when $0\in \partial f(x)$.

The swap test on pure states $|a⟩,|b⟩$ returns ancilla zero with probability

The Hadamard test estimates the real part of $⟨\psi |U|\psi ⟩$ from one ancilla measurement.

The controlled-NOT gate maps $|a,b⟩$ to $|a,b\oplus a⟩$.

Every set lying between a connected set and its closure is connected.

The product of any family of connected spaces is connected. For two factors, fix $y_0$ and use the connected setsThey cover $X\times Y$ and all contain the connected horizontal slice $X\times \{y_0\}$.

If connected subsets cover a space and every two of them intersect, then their union is connected. Equivalently, any continuous integer-valued function is constant on each member, and the intersections force all those constants to agree.