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A one-dimensional elliptic diffusion coefficient has the adjoint gradient
Example
Let with its standard norm, let , and define and . Take . On a neighborhood of each fixed , the form is uniformly coercive in the norm, so this is a parameterized coercive elliptic problem in the sense of Parameter-dependent coercive elliptic variational states and objectives.
Facts & Assumptions
Given: The one-dimensional Sobolev space, the displayed bilinear form/load, and the reduced-gradient formula.
For a uniformly coercive parameterized elliptic variational state and objective, the adjoint formula gives (The elliptic adjoint gives the reduced parameter gradient).
The elliptic variational definition treats states as weak solutions of (Parameter-dependent coercive elliptic variational states and objectives).
The quadratic belongs to (The two-cell hat and quadratic functions belong to H¹₀).
is the closure of in the norm (Zero-boundary Sobolev space as a norm closure, Integer-order Sobolev spaces and their norms).
The integral of the derivative of a C1 scalar function equals its endpoint increment (The second fundamental theorem: if is differentiable on with and is integrable, then ).
Differentiable products obey the product rule (Sums, scalar multiples, products and quotients: , , , and when ).
The integral pairing is bounded by the product of L2 norms (Holder's inequality for integrals, including the endpoint cases).
with the standard inner product is a real Hilbert space (The Sobolev space is a Hilbert space).
Countable Choice is inherited from the state, adjoint, and H0 membership suppliers (The Axiom of Countable Choice ()).
Verification
Put . Direct differentiation gives , , and [F3] gives . By [F9], is a real Hilbert space. Holder's inequality [F8] gives , so ; the objective is bounded linear in and independent of , hence with and . Also for , and is operator-norm , while is constant. For each and , [F5] supplies with . By the product rule [F7], ; since vanishes at both endpoints, [F6] gives . By [F8], the difference between the two sides with in place of is at most , so the weak identity follows as . Also, [F4] gives ; on a neighborhood of each this is a uniform coercivity bound. Hence solves the state equation and is unique. Finally , so .
The adjoint equation is for all , so symmetry of and uniqueness give . The direct parameter derivatives of and vanish. For the scalar parameter direction , . Direct integration gives , hence .
Formula [F1] therefore gives , equal to the derivative of the explicit reduced objective in step 1.1.
Depends on
- The elliptic adjoint gives the reduced parameter gradient
- Parameter-dependent coercive elliptic variational states and objectives
- Integer-order Sobolev spaces and their norms
- Zero-boundary Sobolev space as a norm closure
- The two-cell hat and quadratic functions belong to H¹₀
- Zero-boundary Poincaré inequality on the unit interval
- The second fundamental theorem: if $G$ is differentiable on $[a,b]$ with $G' = f$ and $f$ is integrable, then $\int_a^b f = G(b)-G(a)$
- Sums, scalar multiples, products and quotients: $(f+g)'(c) = f'(c) + g'(c)$, $(\alpha f)'(c) = \alpha f'(c)$, $(fg)'(c) = f'(c)g(c) + f(c)g'(c)$, and $(f/g)'(c) = \bigl(f'(c)g(c) - f(c)g'(c)\bigr)/g(c)^{2}$ when $g(c) \ne 0$
- Holder's inequality for integrals, including the endpoint cases
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- The Sobolev space $H^1$ is a Hilbert space
Used by
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Sources
- Hannes Meinlschmidt, Optimization with Partial Differential Equations, complete lecture notes, §§1.1–1.2 and Example 3.39 (standard reference, not scraped)
- Michael B. Giles and Niles A. Pierce, An Introduction to the Adjoint Approach to Design, §§2.4–3 (standard reference, not scraped)