---
name: operator-theory
description: "Problem-solving strategies for operator theory in functional analysis"
allowed-tools: [Bash, Read]
---

# Operator Theory

## When to Use

Use this skill when working on operator-theory problems in functional analysis.

## Decision Tree


1. **Bounded operator verification**
   - ||Tx|| <= M||x|| for some M
   - Operator norm: ||T|| = sup{||Tx|| : ||x|| = 1}
   - `z3_solve.py prove "operator_bounded"`

2. **Adjoint operator**
   - <Tx, y> = <x, T*y> defines T*
   - For matrices: T* = conjugate transpose
   - `sympy_compute.py simplify "<Tx, y> - <x, T*y>"`

3. **Spectral Theory**
   - Spectrum: sigma(T) = {lambda : T - lambda*I not invertible}
   - Self-adjoint: spectrum is real
   - `z3_solve.py prove "self_adjoint_real_spectrum"`

4. **Compact operators**
   - T compact if T(bounded set) has compact closure
   - Approximable by finite-rank operators
   - `sympy_compute.py limit "||T - T_n||" --var n`

5. **Spectral Theorem**
   - Self-adjoint compact: T = sum(lambda_n * P_n)
   - eigenvalues -> 0, eigenvectors form orthonormal basis


## Tool Commands

### Z3_Bounded_Operator
```bash
uv run python -m runtime.harness scripts/z3_solve.py prove "norm(Tx) <= M*norm(x)"
```

### Sympy_Adjoint
```bash
uv run python -m runtime.harness scripts/sympy_compute.py simplify "<Tx, y> - <x, T_star_y>"
```

### Z3_Spectral
```bash
uv run python -m runtime.harness scripts/z3_solve.py prove "self_adjoint implies real_spectrum"
```

### Sympy_Compact
```bash
uv run python -m runtime.harness scripts/sympy_compute.py limit "norm(T - T_n)" --var n --at oo
```

## Key Techniques

*From indexed textbooks:*

- [Introductory Functional Analysis with Applications] Spectral theory is one of the main branches of modern functional analysis and its applications. Roughly speaking, it is concerned with certain inverse operators, their general properties and their relations to the original operators. Such inverse operators arise quite naturally in connection with the problem of solving equations (systems of linear algebraic equations, differential equations, integral equations).
- [Introductory Functional Analysis with Applications] Unbounded linear operators in Hilb,ert spaces will be considered in Chap. Brief orientation about main content of Chap. We begin with finite dimensional vector spaces.
- [Introductory Functional Analysis with Applications] Most unbounded linear operators occurring in practical problems are closed or have closed linear extensions (Sec. Unbounded Linear Operators in Hilbert Space The spectrum of a self-adjoint linear operator is real, also in the unbounded case (d. T is obtained by means of the Cayley transform U= (T- iI)(T+ iI)-1 of T (d.
- [Introductory Functional Analysis with Applications] Compact Operators and Their Spectrum is called a degenerate kernel. Here we may assume each of the two sets {ab· . If an equation (1) with such a kernel has a solution x, show that it must be of the form n x(s' = ji(s) + lot L cjaj(s), j~l and the unknown constants must satisfy cj - n lot L ajkCk = Yj' k~l where j= 1,···, n.
- [Introductory Functional Analysis with Applications] As indicated before, our key to the application of complex analysis to spectral theory will be Theorem 7. The theorem states that for every value AoEp(n the resolvent R>. TE B(X, X) on a complex Banach space X has a power series repre- sentation (4) R>.

## Cognitive Tools Reference

See `.claude/skills/math-mode/SKILL.md` for full tool documentation.


---
Source attribution: vibeeval
Original project: https://github.com/vibeeval/vibecosystem/blob/3b763b1fb288f57bfa3cce76ef18184b96461a78/skills/math/functional-analysis/operator-theory/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2026 vibeeval

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: vibeeval
Original project: https://github.com/vibeeval/vibecosystem/blob/3b763b1fb288f57bfa3cce76ef18184b96461a78/skills/math/functional-analysis/operator-theory/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Education & Research
Roles: Academic Writer, Data Scientist, Researcher
Specialties: API
Tools: Bash, Read
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 321530ae4c007aa9c4b93c33cb42631f6daf353d602ff1c681ba197ca581c9ef
