A convex function satisfies
Its value on a line segment never lies above the chord joining its endpoint values.
The finite-energy comparison gives . Together with the endpoint value and absolute continuity, this proves .
For , the power competitor is admissible and has finite derivative energy. Direct integration gives
Choose and . The first three terms tend to zero as , while the last equals . By minimality and nonnegativity,
One also obtains the quantitative bound : for , the mean value theorem gives , and . Thus the squared mismatch is at most , matching the order of the derivative penalty with this choice. This is a power-law comparison for vanishing derivative regularization.
A convenient form of the Tonelli existence theorem is: on a finite interval with prescribed endpoint values, if a continuous integrand is convex in and satisfies for some , then, provided a finite-energy admissible competitor exists, its integral attains a minimum in the corresponding class. The lower bound may more generally subtract an integrable function of . Compact one-dimensional Sobolev embedding controls , while convexity gives weak lower semicontinuity in the derivative.
Here and is continuous, convex in , and bounded below by . The competitor has finite energy. Thus for every the Tonelli existence theorem gives an admissible minimizer in .
Any finite-energy member of the larger stated class necessarily has derivative in and bounded function values by absolute continuity; it therefore belongs to . All other competitors have infinite energy. Hence the same function is a global minimizer in the full stated class.
The function has weak derivative and the required endpoint values, so it belongs to the admissible Sobolev space. Its fourth power is , and hence
Since the integrand is nonnegative, zero is a global lower bound. Thus is a global minimizer. Its derivative does not belong to , since its power is proportional to ; positive regularization therefore needs different competitors.
Use , which is admissible for every real . Direct integration gives
The coefficient is strictly negative, so the energy tends to minus infinity as . Therefore
Fix an admissible . The preceding result gives for every . Letting increase to proves
Equality is attained by
Indeed the derivative and function squared integrals are and . These are all equality cases: an equality function minimizes the nonnegative threshold quadratic form, so its first variation vanishes. The resulting weak equation makes smooth and, with the endpoint conditions, yields precisely these multiples. This is the Wirtinger inequality.
Here , and the Jacobi equation is . With and , its solution is for , and for . Its first positive zero is when .
If , no conjugate point occurs in . The Jacobi positivity criterion gives
Since itself is this quadratic form divided by two, for every nonzero admissible . Thus zero is the unique global minimizer. This argument establishes the result before using the sharp Poincaré inequality.
Zero is a strict strong local minimizer, and therefore also a strict weak local minimizer. Here , and
For , Taylor's theorem gives . Also and the Cauchy-Schwarz inequality give . Therefore
The coefficient is positive. Thus every nonzero admissible with has strictly greater energy, proving that zero is a strict strong local minimizer of a variational integral.
Zero is a strict weak local minimizer, but is not a strong local minimizer. Put . Since , the Cauchy-Schwarz inequality gives , and therefore
For sufficiently small , pointwise. Hence
with equality only when . Thus zero is a strict weak local minimizer of a variational integral.
For the strong claim, define a Lipschitz triangular wave
It has zero endpoint values, , and almost everywhere. Thus , while is nonzero on a set of positive measure. Consequently
These uniformly small competitors disprove the strong local minimizer of a variational integral property.
A strong local minimizer of a variational integral is an admissible for which some satisfies
Only the function values must be close; admissible derivatives may be large. Every strong local minimizer is therefore also a weak local minimizer of a variational integral.
A point of convexity of is a point where its tangent line supports the entire graph from below:
The Weierstrass necessary condition says that, at almost every , is a point of convexity of . In terms of the Weierstrass excess function,
Now use the stated derivative coercivity. If were not a local minimizer in , there would be with and . The energy bound gives
The common value controls their function norms as well, by . Thus is bounded in . The one-dimensional compact Sobolev embedding into supplies a uniformly convergent subsequence. Its limit must be , since the same subsequence converges to in . Eventually , contradicting strong local minimality. Hence is also a local minimizer in . This is coercivity transfers strong minimality to L1 minimality.
A weak local minimizer of a variational integral is an admissible for which there is such that
The Sobolev norm controls both the function and its first weak derivative. The boundary values remain fixed because both functions are admissible.
Zero is a strict weak local minimizer, but is not a strong local minimizer. The derivative integrand has expansion , so for small it is at least . Concavity gives for every real . Therefore, when is small,
using . This proves strict weak minimality.
For the strong claim, let . Its amplitude tends to zero and its derivative is almost everywhere, so the derivative integrand is the constant . The other integrand term is at most . Hence
This disproves strong minimality. The same obstruction is detected by the Weierstrass necessary condition, since the supporting-tangent inequality at slope zero fails at .
Zero is neither a weak nor a strong local minimizer. For any nonzero smooth zero-trace , Taylor expansion gives
The mixed integral by the endpoint values. Writing and , the quadratic coefficient is . The elementary estimate on makes this at most . Thus arbitrarily small variations decrease the energy. The separate coefficient one on is retained in this calculation.
Zero is neither a weak nor a strong local minimizer. Take . It tends to zero in and has zero endpoint values. Since for ,
for sufficiently small positive . Thus even weakly close admissible competitors decrease the energy.
A strong local minimizer of a variational integral is an admissible for which the same conclusion holds whenever . Every strong local minimizer is weak, because Sobolev closeness implies uniform closeness. A global minimizer is both, and the converse implication from weak to strong is not generally valid.
If the integrand is independent of , a weak local minimizer has vanishing first variation:
The bounded function therefore has zero distributional derivative, so is constant almost everywhere. For an elementary test-function argument, any smooth compactly supported of integral zero is the derivative of a compactly supported smooth . Hence ; subtracting a fixed unit-integral test function from arbitrary tests shows that agrees with one constant against every test. The fundamental lemma then gives
This is the first integral for a variational integrand independent of the function value.
A point of convexity of a differentiable real function has a globally supporting tangent: for every . The Weierstrass necessary condition for a strong minimum says that is such a point for for almost every . Equivalently, the Weierstrass excess function is nonnegative at every trial slope.
A weak local minimizer of a variational integral is an admissible for which some makes whenever and . Both the function and its derivative are required to be close.
Choose any nonzero admissible and set . Then
The coefficient of is strictly positive, so the energy tends to minus infinity as . Hence
For every , positivity in the preceding part gives for each fixed admissible . Let to obtain
The sharp constant is attained by , consistently with the first conjugate point occurring at the endpoint for .
At zero, the sixth-power term has zero second derivative, so
For , the Jacobi solution is strictly positive on . The preceding identity gives for nonzero zero-trace .
To control the nonlinear remainder, a uniform lower bound follows directly. Let , , and . Since and by the elementary endpoint estimate,
Thus with . Also , so
This is positive for every nonzero admissible sufficiently close to zero in . Therefore zero has positive second variation and is a strict weak local minimizer. No sharp Poincaré inequality has been assumed in this proof.
If an admissible attained the infimum, nonnegativity and would force almost everywhere, so its continuous Sobolev representative would be identically zero. Its derivative would then vanish almost everywhere, but the other integrand term would equal . This is impossible. Equivalently, zero energy would simultaneously require and almost everywhere. Thus

Pinned article: Introduction to the OurBigBook Project

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