The Kantorovich theorem, or Newton–Kantorovich theorem, is a mathematical statement on the semi-local convergence of Newton's method. It was first stated by Leonid Kantorovich in 1948.^[1][2] It is similar to the form of the Banach fixed-point theorem, although it states existence and uniqueness of a zero rather than a fixed point.^[3]

Newton's method constructs a sequence of points that under certain conditions will converge to a solution ${\displaystyle x}$ of an equation ${\displaystyle f(x)=0}$ or a vector solution of a system of equation ${\displaystyle F(x)=0}$. The Kantorovich theorem gives conditions on the initial point of this sequence. If those conditions are satisfied then a solution exists close to the initial point and the sequence converges to that point.^[1][2]

Assumptions

Let ${\displaystyle X\subset \mathbb {R} ^{n))$ be an open subset and ${\displaystyle F:X\subset \mathbb {R} ^{n}\to \mathbb {R} ^{n))$ a differentiable function with a Jacobian ${\displaystyle F^{\prime }(\mathbf {x} )}$ that is locally Lipschitz continuous (for instance if ${\displaystyle F}$ is twice differentiable). That is, it is assumed that for any ${\displaystyle x\in X}$ there is an open subset ${\displaystyle U\subset X}$ such that ${\displaystyle x\in U}$ and there exists a constant ${\displaystyle L>0}$ such that for any ${\displaystyle \mathbf {x} ,\mathbf {y} \in U}$

${\displaystyle \|F'(\mathbf {x} )-F'(\mathbf {y} )\|\leq L\;\|\mathbf {x} -\mathbf {y} \|}$

holds. The norm on the left is the operator norm. In other words, for any vector ${\displaystyle \mathbf {v} \in \mathbb {R} ^{n))$ the inequality

${\displaystyle \|F'(\mathbf {x} )(\mathbf {v} )-F'(\mathbf {y} )(\mathbf {v} )\|\leq L\;\|\mathbf {x} -\mathbf {y} \|\,\|\mathbf {v} \|}$

must hold.

Now choose any initial point ${\displaystyle \mathbf {x} _{0}\in X}$. Assume that ${\displaystyle F'(\mathbf {x} _{0})}$ is invertible and construct the Newton step ${\displaystyle \mathbf {h} _{0}=-F'(\mathbf {x} _{0})^{-1}F(\mathbf {x} _{0}).}$

The next assumption is that not only the next point ${\displaystyle \mathbf {x} _{1}=\mathbf {x} _{0}+\mathbf {h} _{0))$ but the entire ball ${\displaystyle B(\mathbf {x} _{1},\|\mathbf {h} _{0}\|)}$ is contained inside the set ${\displaystyle X}$. Let ${\displaystyle M}$ be the Lipschitz constant for the Jacobian over this ball (assuming it exists).

As a last preparation, construct recursively, as long as it is possible, the sequences ${\displaystyle (\mathbf {x} _{k})_{k))$, ${\displaystyle (\mathbf {h} _{k})_{k))$, ${\displaystyle (\alpha _{k})_{k))$ according to

${\displaystyle {\begin{alignedat}{2}\mathbf {h} _{k}&=-F'(\mathbf {x} _{k})^{-1}F(\mathbf {x} _{k})\\[0.4em]\alpha _{k}&=M\,\|F'(\mathbf {x} _{k})^{-1}\|\,\|\mathbf {h} _{k}\|\\[0.4em]\mathbf {x} _{k+1}&=\mathbf {x} _{k}+\mathbf {h} _{k}.\end{alignedat))}$

Statement

Now if ${\displaystyle \alpha _{0}\leq {\tfrac {1}{2))}$ then

1. a solution ${\displaystyle \mathbf {x} ^{*))$ of ${\displaystyle F(\mathbf {x} ^{*})=0}$ exists inside the closed ball ${\displaystyle {\bar {B))(\mathbf {x} _{1},\|\mathbf {h} _{0}\|)}$ and
2. the Newton iteration starting in ${\displaystyle \mathbf {x} _{0))$ converges to ${\displaystyle \mathbf {x} ^{*))$ with at least linear order of convergence.

A statement that is more precise but slightly more difficult to prove uses the roots ${\displaystyle t^{\ast }\leq t^{**))$ of the quadratic polynomial

${\displaystyle p(t)=\left({\tfrac {1}{2))L\|F'(\mathbf {x} _{0})^{-1}\|^{-1}\right)t^{2}-t+\|\mathbf {h} _{0}\|}$,

${\displaystyle t^{\ast /**}={\frac {2\|\mathbf {h} _{0}\|}{1\pm {\sqrt {1-2\alpha _{0))))))$

and their ratio

${\displaystyle \theta ={\frac {t^{*)){t^{**))}={\frac {1-{\sqrt {1-2\alpha _{0)))){1+{\sqrt {1-2\alpha _{0))))}.}$

Then

1. a solution ${\displaystyle \mathbf {x} ^{*))$ exists inside the closed ball ${\displaystyle {\bar {B))(\mathbf {x} _{1},\theta \|\mathbf {h} _{0}\|)\subset {\bar {B))(\mathbf {x} _{0},t^{*})}$
2. it is unique inside the bigger ball ${\displaystyle B(\mathbf {x} _{0},t^{*\ast })}$
3. and the convergence to the solution of ${\displaystyle F}$ is dominated by the convergence of the Newton iteration of the quadratic polynomial ${\displaystyle p(t)}$ towards its smallest root ${\displaystyle t^{\ast ))$,^[4] if ${\displaystyle t_{0}=0,\,t_{k+1}=t_{k}-{\tfrac {p(t_{k})}{p'(t_{k})))}$, then

   ${\displaystyle \|\mathbf {x} _{k+p}-\mathbf {x} _{k}\|\leq t_{k+p}-t_{k}.}$

4. The quadratic convergence is obtained from the error estimate^[5]

   ${\displaystyle \|\mathbf {x} _{n+1}-\mathbf {x} ^{*}\|\leq \theta ^{2^{n))\|\mathbf {x} _{n+1}-\mathbf {x} _{n}\|\leq {\frac {\theta ^{2^{n))}{2^{n))}\|\mathbf {h} _{0}\|.}$

Corollary

In 1986, Yamamoto proved that the error evaluations of the Newton method such as Doring (1969), Ostrowski (1971, 1973),^[6][7] Gragg-Tapia (1974), Potra-Ptak (1980),^[8] Miel (1981),^[9] Potra (1984),^[10] can be derived from the Kantorovich theorem.^[11]

Generalizations

There is a q-analog for the Kantorovich theorem.^[12][13] For other generalizations/variations, see Ortega & Rheinboldt (1970).^[14]

Applications

Oishi and Tanabe claimed that the Kantorovich theorem can be applied to obtain reliable solutions of linear programming.^[15]