RadiiPolynomial

2.2 Radii Polynomial Approach on Banach Spaces

This section extends the radii polynomial method to infinite-dimensional Banach spaces, allowing for maps between potentially different spaces \(E\) and \(F\).

2.2.1 Banach Space Setup

We work with two Banach spaces \(E\) and \(F\) over \(\mathbb {R}\). For each space \(X \in \{ E, F\} \):

(1) NormedAddCommGroup X: \(X\) has a norm satisfying definiteness, symmetry, triangle inequality

(2) NormedSpace \(\mathbb {R}\) X: The norm is compatible with scalar multiplication

(3) CompleteSpace X: Every Cauchy sequence converges (crucial for fixed point theorems)

This framework supports:

(4) Fréchet derivatives (via the norm structure)

(5) Fixed point theorems (via completeness)

(6) Mean Value Theorem (via the metric structure)

(7) Linear operator theory (via the vector space structure)

2.2.2 Neumann Series and Operator Invertibility

The Neumann series provides a constructive way to show operators close to the identity are invertible.

Theorem 2.2.1 Neumann series invertibility
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Let \(E\) be a Banach space and \(B: E \rightarrow E\) a continuous linear operator. If \(\| I_E - B\| {\lt} 1\), then \(B\) is invertible (a unit in the multiplicative sense).

Proof

Write \(B = I_E - (I_E - B)\). Since \(\| I_E - B \| {\lt} 1\), the Neumann series \(\sum _{n=0}^{\infty } (I_E - B)^n\) converges absolutely to \((I_E - (I_E - B))^{-1} = B^{-1}\). This is a direct application of Mathlib’s isUnit_one_sub_of_norm_lt_one.

Lemma 2.2.2 Explicit two-sided inverse
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If \(\| I_E - B\| {\lt} 1\) for \(B: E \rightarrow E\), then there exists \(B^{-1}: E \rightarrow E\) such that

\begin{equation*} B \circ B^{-1} = I_E \quad \text{and} \quad B^{-1} \circ B = I_E. \end{equation*}
Lemma 2.2.3 Composition form
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If \(\| I_E - B\| {\lt} 1\), then there exists \(B^{-1}\) such that

\begin{equation*} B.\text{comp}(B^{-1}) = I_E \quad \text{and} \quad B^{-1}.\text{comp}(B) = I_E. \end{equation*}

2.2.3 Newton-Like Operators for E to F Maps

Definition 2.2.4 Newton-like map for E to F
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For a function \(f: E \rightarrow F\) and an approximate inverse \(A: F \rightarrow E\), the Newton-like map is

\begin{equation*} T(x) = x - A(f(x)). \end{equation*}

Note that \(T: E \rightarrow E\) even though \(f\) maps between different spaces.

Proposition 2.2.5 Fixed points \(\Leftrightarrow \) Zeros for E to F

Let \(f: E \rightarrow F\) and \(A: F \rightarrow E\) be injective. Then for the Newton-like operator \(T(x) = x - A(f(x))\):

\begin{equation*} T(x) = x \quad \Leftrightarrow \quad f(x) = 0. \end{equation*}

This holds even when \(E \neq F\); injectivity of \(A\) is sufficient.

2.2.4 Radii Polynomial Definitions

Definition 2.2.6 General radii polynomial
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For constants \(Y_0, Z_0, Z_1 \geq 0\) and function \(Z_2: (0,\infty ) \rightarrow [0,\infty )\), the general radii polynomial is

\begin{equation*} p(r) := Z_2(r)r^2 - (1 - Z_0 - Z_1)r + Y_0. \end{equation*}
Definition 2.2.7 Combined bound (general case)
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The combined bound is

\begin{equation*} Z(r) := Z_0 + Z_1 + Z_2(r) \cdot r. \end{equation*}
Lemma 2.2.8 Alternative form (general)

The general radii polynomial can be rewritten as \(p(r) = (Z(r) - 1)r + Y_0\).

Lemma 2.2.9 General polynomial negativity implies contraction

If \(Y_0 \geq 0\), \(r_0 {\gt} 0\), and \(p(r_0) {\lt} 0\) for the general radii polynomial, then \(Z(r_0) {\lt} 1\).

Definition 2.2.10 Simple radii polynomial
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For \(Y_0 \geq 0\) and \(Z: (0,\infty ) \rightarrow [0,\infty )\), the simple radii polynomial is

\begin{equation*} p(r) := (Z(r) - 1)r + Y_0. \end{equation*}
Lemma 2.2.11 Simple polynomial negativity implies contraction

If \(Y_0 \geq 0\), \(r_0 {\gt} 0\), and \(p(r_0) {\lt} 0\) for the simple radii polynomial, then \(Z(r_0) {\lt} 1\).

2.2.5 Operator Bounds

Lemma 2.2.12 \(Y_0\) bound for Newton operator
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If \(\| A(f(\bar{x}))\| \leq Y_0\), then for \(T(x) = x - A(f(x))\): \(\| T(\bar{x}) - \bar{x}\| \leq Y_0\).

Proof

\(\| T(\bar{x}) - \bar{x}\| = \| (\bar{x} - A(f(\bar{x}))) - \bar{x}\| = \| -A(f(\bar{x}))\| = \| A(f(\bar{x}))\| \leq Y_0\).

Lemma 2.2.13 Derivative of Newton operator
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For \(T(x) = x - A(f(x))\) where \(f: E \rightarrow F\) is differentiable: \(\text{DT}(x) = I_E - A \circ \text{Df}(x)\).

Proof

By the chain rule: \(\text{D}[x \mapsto A(f(x))] = A \circ \text{Df}(x)\). Since \(\text{D}[\text{id}] = I_E\), we have \(\text{DT}(x) = I_E - A \circ \text{Df}(x)\).

Lemma 2.2.14 General derivative bound

Suppose for all \(c \in \overline{B}_r(\bar{x})\):

\begin{equation*} \| I_E - A \circ A^\dagger \| \leq Z_0 \end{equation*}
\begin{equation*} \| A \circ (A^\dagger - \text{Df}(\bar{x}))\| \leq Z_1 \end{equation*}
\begin{equation*} \| A \circ (\text{Df}(c) - \text{Df}(\bar{x}))\| \leq Z_2(r) \cdot r \end{equation*}

Then \(\| \text{DT}(c)\| \leq Z_0 + Z_1 + Z_2(r) \cdot r = Z(r)\).

Proof

Decompose using \(A^\dagger \):

\begin{equation*} I_E - A \circ \text{Df}(c) = [I_E - A \circ A^\dagger ] + A \circ [A^\dagger - \text{Df}(\bar{x})] + A \circ [\text{Df}(\bar{x}) - \text{Df}(c)]. \end{equation*}

By triangle inequality: \(\| \text{DT}(c)\| \leq Z_0 + Z_1 + Z_2(r) \cdot r\).

Lemma 2.2.15 Simple derivative bound

When \(A^\dagger = \text{Df}(\bar{x})\) (so \(Z_1 = 0\)), for all \(c \in \overline{B}_r(\bar{x})\): if \(\| I_E - A \circ \text{Df}(\bar{x})\| \leq Z_0\) and \(\| A \circ (\text{Df}(c) - \text{Df}(\bar{x}))\| \leq Z_2(r) \cdot r\), then \(\| \text{DT}(c)\| \leq Z_0 + Z_2(r) \cdot r\).

2.2.6 Helper Lemmas

Lemma 2.2.16 Closed balls are complete
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In a complete space \(E\), closed balls \(\overline{B}_r(x)\) are complete.

Lemma 2.2.17 Extended distance is finite
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In normed spaces, extended distance is always finite: \(d_{\text{ext}}(x,y) \neq \top \).

Lemma 2.2.18 Constructing derivative inverse
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If \(A: F \rightarrow E\) is injective and \(\| I_E - A \circ B\| {\lt} 1\) for \(B: E \rightarrow F\), then \(B\) is invertible with inverse \(B^{-1} = (A \circ B)^{-1} \circ A\).

Proof

By Lemma 2.2.3, \(A \circ B\) is invertible with inverse \((A \circ B)^{-1}\). Let \(B^{-1} = (A \circ B)^{-1} \circ A\).

Left inverse: \(B(B^{-1}(x)) = B((A \circ B)^{-1}(A(x)))\). Apply \(A\): \(A(B(B^{-1}(x))) = (A \circ B)((A \circ B)^{-1}(A(x))) = A(x)\). By injectivity of \(A\): \(B(B^{-1}(x)) = x\).

Right inverse: \(B^{-1}(B(x)) = (A \circ B)^{-1}(A(B(x))) = (A \circ B)^{-1}((A \circ B)(x)) = x\).

Lemma 2.2.19 Ball mapping property

Given \(T: E \rightarrow E\) differentiable with: (a) \(\| T(\bar{x}) - \bar{x}\| \leq Y_0\); (b) \(\| \text{DT}(c)\| \leq Z(r_0)\) for all \(c \in \overline{B}_{r_0}(\bar{x})\); (c) \(Z(r_0) \geq 0\); (d) \(p(r_0) = (Z(r_0) - 1)r_0 + Y_0 {\lt} 0\). Then \(T: \overline{B}_{r_0}(\bar{x}) \rightarrow \overline{B}_{r_0}(\bar{x})\).

Proof

From \(p(r_0) {\lt} 0\): \(Z(r_0) \cdot r_0 + Y_0 {\lt} r_0\). The segment \([\bar{x}, x]\) lies in \(\overline{B}_{r_0}(\bar{x})\) by convexity. By Mean Value Theorem: \(\| T(x) - T(\bar{x})\| \leq Z(r_0) \cdot \| x - \bar{x}\| \leq Z(r_0) \cdot r_0\). By triangle inequality:

\begin{equation*} \| T(x) - \bar{x}\| \leq \| T(x) - T(\bar{x})\| + \| T(\bar{x}) - \bar{x}\| \leq Z(r_0) \cdot r_0 + Y_0 {\lt} r_0. \end{equation*}

2.2.7 Main Theorems

Theorem 2.2.20 General Fixed Point Theorem (Theorem 7.6.1)

Let \(T: E \rightarrow E\) be Fréchet differentiable and \(\bar{x} \in E\). Suppose:

\begin{equation*} \| T(\bar{x}) - \bar{x}\| \leq Y_0 \end{equation*}
\begin{equation*} \| \text{DT}(x)\| \leq Z(r) \quad \text{for all } x \in \overline{B}_r(\bar{x}) \end{equation*}

Define \(p(r) := (Z(r) - 1)r + Y_0\).

If there exists \(r_0 {\gt} 0\) such that \(p(r_0) {\lt} 0\), then there exists a unique \(\tilde{x} \in \overline{B}_{r_0}(\bar{x})\) such that \(T(\tilde{x}) = \tilde{x}\).

Proof

Step 1: From \(p(r_0) {\lt} 0\) and Lemma 2.2.11: \(Z(r_0) {\lt} 1\). Also \(Z(r_0) \geq 0\) since \(\| \text{DT}(\bar{x})\| \geq 0\).

Step 2: By Lemma 2.2.19, \(T: \overline{B}_{r_0}(\bar{x}) \rightarrow \overline{B}_{r_0}(\bar{x})\).

Step 3: \(T\) restricted to \(\overline{B}_{r_0}(\bar{x})\) is a contraction with constant \(Z(r_0) {\lt} 1\). For \(x, y \in \overline{B}_{r_0}(\bar{x})\), by Mean Value Theorem: \(\| T(x) - T(y)\| \leq Z(r_0) \| x - y\| \).

Step 4: The closed ball is complete by Lemma 2.2.16.

Step 5: Apply Theorem 1.1.3 to get unique fixed point.

Theorem 2.2.21 General Radii Polynomial Theorem (Theorem 7.6.2)

Let \(E\) and \(F\) be Banach spaces and \(f: E \rightarrow F\) be Fréchet differentiable. Suppose \(\bar{x} \in E\), \(A^\dagger : E \rightarrow F\), and \(A: F \rightarrow E\) with \(A\) injective. Assume:

\begin{equation*} \| A(f(\bar{x}))\| \leq Y_0 \end{equation*}
\begin{equation*} \| I_E - A \circ A^\dagger \| \leq Z_0 \end{equation*}
\begin{equation*} \| A \circ [\text{Df}(\bar{x}) - A^\dagger ]\| \leq Z_1 \end{equation*}
\begin{equation*} \| A \circ [\text{Df}(c) - \text{Df}(\bar{x})]\| \leq Z_2(r) \cdot r \quad \text{for } c \in \overline{B}_r(\bar{x}) \end{equation*}

Define \(p(r) := Z_2(r)r^2 - (1 - Z_0 - Z_1)r + Y_0\).

If there exists \(r_0 {\gt} 0\) such that \(p(r_0) {\lt} 0\), then there exists a unique \(\tilde{x} \in \overline{B}_{r_0}(\bar{x})\) with \(f(\tilde{x}) = 0\).

Proof

Step 1: Let \(T(x) = x - A(f(x))\). Then \(T: E \rightarrow E\) is differentiable.

Step 2: By Lemma 2.2.12: \(\| T(\bar{x}) - \bar{x}\| \leq Y_0\). By Lemma 2.2.14: \(\| \text{DT}(c)\| \leq Z(r_0)\) for \(c \in \overline{B}_{r_0}(\bar{x})\). By Lemma 2.2.8: \(p(r_0) = (Z(r_0) - 1)r_0 + Y_0 {\lt} 0\).

Step 3: Apply Theorem 2.2.20: unique \(\tilde{x} \in \overline{B}_{r_0}(\bar{x})\) with \(T(\tilde{x}) = \tilde{x}\).

Step 4: By Proposition 2.2.5 with injectivity of \(A\): \(f(\tilde{x}) = 0\).

Theorem 2.2.22 Simple Radii Polynomial for E to F

Given \(f: E \rightarrow F\) Fréchet differentiable and injective \(A: F \rightarrow E\) satisfying \(\| A(f(\bar{x}))\| \leq Y_0\), \(\| I_E - A \circ \text{Df}(\bar{x})\| \leq Z_0\), and \(\| A \circ [\text{Df}(c) - \text{Df}(\bar{x})]\| \leq Z_2(r) \cdot r\) for \(c \in \overline{B}_r(\bar{x})\). If \(p(r_0) = Z_2(r_0)r_0^2 - (1-Z_0)r_0 + Y_0 {\lt} 0\), then there exists unique \(\tilde{x} \in \overline{B}_{r_0}(\bar{x})\) with \(f(\tilde{x}) = 0\).

Theorem 2.2.23 Simple Radii Polynomial (Same Space)

Consider \(f: E \rightarrow E\) Fréchet differentiable, \(\bar{x} \in E\), and \(A: E \rightarrow E\) injective. Assume \(\| A(f(\bar{x}))\| \leq Y_0\), \(\| I_E - A \circ \text{Df}(\bar{x})\| \leq Z_0\), and \(\| A \circ [\text{Df}(c) - \text{Df}(\bar{x})]\| \leq Z_2(r) \cdot r\) for \(c \in \overline{B}_r(\bar{x})\). Define \(p(r) := Z_2(r)r^2 - (1-Z_0)r + Y_0\).

If \(p(r_0) {\lt} 0\), then there exists unique \(\tilde{x} \in \overline{B}_{r_0}(\bar{x})\) with \(f(\tilde{x}) = 0\) and \(\text{Df}(\tilde{x})\) invertible.

Proof

Steps 1–4: As in Theorem 2.2.21, get fixed point \(\tilde{x}\) with \(f(\tilde{x}) = 0\).

Step 5 (Invertibility): Since \(\tilde{x} \in \overline{B}_{r_0}(\bar{x})\) and \(Z(r_0) {\lt} 1\) (by Lemma 1.4.5), \(\| I_E - A \circ \text{Df}(\tilde{x})\| \leq Z(r_0) {\lt} 1\). Apply Lemma 2.2.18: \(\text{Df}(\tilde{x})\) is invertible.