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A subgradient of a convex function $f$ at $x$ is a vector $g$ such that $f(y)\ge f(x)+g^T(y-x)$ for every $y$ in the domain.

A nonsmooth gradient flow driven by the subdifferential of a convex function. Explicit discretization gives the forward subgradient step; implicit discretization gives the proximal operator. Monotonicity of a convex subdifferential underlies uniqueness and contraction properties.

The subgradient method minimizes a possibly nonsmooth convex function by choosing $g_k\in \partial f(x_k)$ and iteratingIf the subgradients are bounded by $G$ and a minimizer is within distance $R$ of $x_0$, a suitable constant or diminishing step size finds objective error at most $ϵ$ in $O(R^2{G}^2/{ϵ}^2)$ iterations.

A continuous function on an open interval with positive symmetric derivative everywhere is strictly increasing. One proof takes an antiderivative $F$ using the fundamental theorem of calculus. For small positive $h$, the identityfollows from $f^s(x)>0$. If $F$ exceeded a chord, its difference from the chord would attain a positive interior maximum, contradicting this symmetric difference inequality. Thus $F$ is a convex function, and its derivative $f$ is nondecreasing. Equality at two different points would make $f$ constant between them and force an interior symmetric derivative to vanish, proving strict monotonicity.

As a quadrature rule, the trapezoidal rule replaces an integrand by the linear interpolation joining its two endpoint values:For nonnegative endpoint values, this is the area of a trapezoid; in general it is a signed area. The quadrature nodes are $a,b$, each with quadrature weight $(b-a)/2$. Unlike the midpoint quadrature rule, it uses endpoint samples, which can be reused between adjacent panels.

Every affine function $f(x)=\alpha x+\beta$ agrees with its linear interpolation, so the trapezoidal rule integrates it exactly. On $[0,2]$, $f(x)=2x+1$ gives $T(f)=(2/2)(1+5)=6=I(f)$. On $[0,1]$, the polynomial $f(x)=x^2$ instead gives $T(f)=1/2$, exceeding $I(f)=1/3$ by $1/6$. Its degree of precision of a quadrature rule is exactly one.

If $f\in C^2[a,b]$, its signed approximation error satisfiesA convex function lies below its endpoint chord, so the trapezoidal rule overestimates its integral; a concave function gives the reverse inequality. The error formula assumes that the second derivative exists and is a continuous function throughout the closed interval.^[ref] A short derivation is the identityobtained by two integrations by parts. The nonnegative Peano kernel has integral $(b-a{)}^3/12$, and the weighted mean value theorem for integrals gives the formula.

The trapezoidal rule for an ordinary differential equation averages vector fields at the old and new states and is A-stable.

This time-stepping use applies the same trapezoidal rule to the integral of the derivative in an ordinary differential equation. That connection explains its place under implicit Runge-Kutta methods; its quadrature behavior and the nonlinear issue in trapezoidal rule fails B-stability concern different uses of the same endpoint averaging.

In the common PDE convention, a uniformly convex scalar flux is a $C^2$ flux satisfying $F^{\prime \prime }(z)\ge \kappa >0$ throughout the specified state interval, with one constant $\kappa$. Equivalently, $F(z)-\kappa z^2/2$ is a convex function. This guarantees strict inequality below nontrivial chords and strict ordering of the two endpoint derivatives around the chord slope. On the whole real line it is incompatible with a globally bounded first derivative.

For an increasing concave utility on positive wealth, its utility conjugate uses the indicated supremum and is a convex function of the positive price variable. If $F(x)=-U(x)$ on positive $x$, extended by infinity elsewhere, then $\hat{U}(y)=F^{\ast }(-y)$. Under the Inada conditions its optimizer is the inverse marginal utility, and its derivative is the negative of that optimizer. The sign convention is part of the definition.

For a feasible set $K$ and a vector field $F$, a variational inequality asks for $x^{\ast }\in K$ satisfying the displayed inequality for every feasible $x$. If $K$ is convex and $F$ is the gradient of a differentiable convex function, it is the necessary and sufficient first-order condition for minimizing that function. It also describes Wardrop equilibria when no scalar objective has the route-cost vector as its gradient.

This excess measures the height of the slope-dependent integrand above its tangent line at the reference slope $p$, evaluated at a trial slope $q$. A convex function of slope has nonnegative excess. At a smooth strong local minimizer, localized needle variations force nonnegative excess along the minimizing curve for every trial slope. It detects fast-gradient competitors that the second variation around a fixed derivative can miss.