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Relaxed objective for a partially one-homogeneous projection
Definition
Assume AC. Let be a measurable space, let be a real Hilbert space, let be convex and differentiable, let be bounded and the pointwise norm limit on all of of a sequence of -measurable, finite-valued simple functions, and let be bounded and measurable. For each finite signed measure on with , write for its Jordan decomposition into mutually singular finite nonnegative measures, so . Define . Both Bochner integrals exist because the same simple approximation makes strongly measurable for every measure on , while boundedness makes its norm integrable for each finite measure. Scalar integrals against use the corresponding signed-measure integral. Assume and . Define
For every finite nonnegative measure on with and , its feature moment and penalty are and , respectively, and its feature energy is
Then . Equality is attained by the finite nonnegative measure defined on measurable by
Thus .
Definition references
Feature-measure energy and its regularity assumptions
Partially one-homogeneous signed measure projection
Bounded linear maps commute with Bochner integration
Jordan decomposition of a signed measure into unique mutually singular positive parts
The space of finite total variation signed measures
The total variation |nu|(E) from countable measurable partitions
Integration against a signed or complex measure, and the class L^1(nu) = L^1(|nu|)
Bochner integrability criterion
Monotonicity and nonnegative homogeneity of the nonnegative integral
Bochner integrability criterion
Facts & Assumptions
Given: AC, the model, a finite signed measure , and the signed projection in the Definition.
AC supplies the Jordan decomposition of each finite signed measure. The Axiom of Choice Jordan decomposition of a signed measure into unique mutually singular positive parts
, with ; for every bounded measurable scalar , its pairing is , and has finite total variation. Partially one-homogeneous signed measure projection
A bounded linear map commutes with Bochner integration, so feature moments are determined by all scalar evaluations. Bounded linear maps commute with Bochner integration
Scalar signed integrals are defined by approximation with simple functions; bounded measurable scalar functions are integrable against finite signed measures. Integration against a signed or complex measure, and the class L^1(nu) = L^1(|nu|)
Total variation is the supremum of the sums over countable measurable partitions. The total variation |nu|(E) from countable measurable partitions
The nonnegative integral is monotone. Monotonicity and nonnegative homogeneity of the nonnegative integral
For the Jordan decomposition, total variation satisfies . For a signed measure, total variation is nu-plus plus nu-minus, finite partitions suffice, and nu-plus and nu-minus are extremal
Strongly measurable Banach-valued functions with integrable norm have Bochner integrals defined by simple-function approximation. Bochner integrability criterion
Proof
Given: AC, , and as in the Definition.
Let be the raw measures in [F1]. For every countable measurable partition of a measurable set , . Taking the supremum in [F3] gives . For a nonnegative simple , the definitions of give . If bounds , partition into finitely many intervals of length at most and assign an endpoint on each measurable inverse image. This gives nonnegative simple with . The error on the left is at most , and the error on the right is at most ; these bounds agree because . Taking proves the identity for . Monotonicity [F4] and the measure domination, with a measure by [F7], give .
The pointwise simple approximation in the Definition makes strongly measurable for the finite measure . Multiplying those approximants by scalar simple functions converging pointwise to gives a pointwise simple approximation to on the product. Its norm is bounded by , so both this weighted feature and are Bochner integrable for the relevant measures by [F6]. For every , the function is bounded and measurable. The pairing [F1] and equality give . By [F2] and [F5], these are respectively and , so equality for every proves equality of feature moments. Adding the risk term to step 1.1 proves . The section maps and are measurable: preimages of measurable rectangles are either empty or measurable subsets of . Their preimages preserve disjoint unions, so the displayed formula defines a finite nonnegative measure, of mass . The definition of gives by [A1]. Direct integration on the two sections gives and . Hence . This proves the infimum identity and its attainment.
Depends on
- Feature-measure energy and its regularity assumptions
- Partially one-homogeneous signed measure projection
- Bounded linear maps commute with Bochner integration
- The Axiom of Choice
- Jordan decomposition of a signed measure into unique mutually singular positive parts
- The space of finite total variation signed measures
- The total variation |nu|(E) from countable measurable partitions
- Integration against a signed or complex measure, and the class L^1(nu) = L^1(|nu|)
- Bochner-integrable function
- Bochner integrability criterion
- For a signed measure, total variation is nu-plus plus nu-minus, finite partitions suffice, and nu-plus and nu-minus are extremal
- Monotonicity and nonnegative homogeneity of the nonnegative integral
Used by
- A W2 limit in the partial one-homogeneous case is globally minimizing Corollary
- A partial signed projection limit has convergent penalty and energy Lemma
- Nonoptimal partial projections have an escaping sign branch Lemma
- Signwise first-variation conditions minimize the relaxed objective Lemma
- Stationarity of a partially homogeneous signed projection limit Lemma
- A partially homogeneous signed projection limit is a minimizer Theorem
Dependency tree · two levels
38 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Lénaïc Chizat and Francis Bach, On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport (standard reference, not scraped)
- Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré, Gradient Flows in Metric Spaces and in the Space of Probability Measures (standard reference, not scraped)