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A partially homogeneous signed projection limit is a minimizer
Statement
Assume AC, a separable real Hilbert space , and the feature-energy hypotheses: is convex and differentiable with locally Lipschitz differential bounded on sublevels, is Fr\acute{e}chet differentiable, is semiconvex, and the nested cutoff sets and derivative-growth bounds in the feature-energy definition hold. Let , , be bounded and continuously differentiable with Lipschitz differential, and be bounded and continuously differentiable with Lipschitz differential; set and . For every , require dense regular values for . Retain either (i) is the closure of a nonempty bounded connected open convex set with boundary and on for every , or (ii) and for every , converges uniformly in to a limit with dense regular values as . Let be a Wasserstein gradient flow whose initial support is contained in and separates the faces and . If converges weakly against every bounded continuous real test function to the finite signed measure , then globally minimizes the relaxed objective and .
Statement references
Feature-measure energy and its regularity assumptions
Wasserstein gradient flow for a feature-measure energy
Partially one-homogeneous signed measure projection
Relaxed objective for a partially one-homogeneous projection
Stationarity of a partially homogeneous signed projection limit
Nonoptimal partial projections have an escaping sign branch
Partial-homogeneity flows preserve box separation
Facts & Assumptions
Given: AC and the full hypotheses in the Statement.
The relaxed objective is the infimum of the lifted energy at fixed signed projection and admits the Jordan-sign minimizing lift. Relaxed objective for a partially one-homogeneous projection
Signed narrow projection convergence gives norm moments, BL convergence, and signwise stationarity at the limit. Signed narrow convergence controls variation tails and Bochner moments Stationarity of a partially homogeneous signed projection limit
Nonnegative signwise first variations plus signwise stationarity characterize a minimizer of the relaxed objective. Signwise first-variation conditions minimize the relaxed objective
Favorable positive mass in a negative inner sign sublevel forces finite-time exit from a fixed BL neighborhood. Nonoptimal partial projections have an escaping sign branch
On every finite horizon the support is the closed image of the initial support by continuous characteristics, remains in a bounded weight slab, and meets every vertical line. Partial-homogeneity flows preserve box separation One-sided semiconvex characteristics give W2 uniqueness and stability The global Wasserstein gradient flow exists uniquely and is stable
The full hypotheses force . Negative sublevels have universal robust outer traps, including for reversed-time close forcing. The full global partial-model hypotheses force a constant penalty coefficient Negative partial-homogeneity levels trap all sufficiently close forcings
The actual lifted penalty and energy converge to the Jordan penalty and under the asserted signed narrow limit. A partial signed projection limit has convergent penalty and energy
A finite-energy finite nonnegative measure whose first variation is nonnegative everywhere and zero almost everywhere for that measure minimizes over all finite nonnegative Borel measures. The comparison requires Bochner integrability of and integrability of for finite-energy competitors, without a -moment or second-moment requirement. First variation and optimality over nonnegative measures
Proof
Given: The flow and signed projection in the Statement.
Use the natural slabs ; their cutoff and bounded supporting-selection hypotheses are verified in the box-separation proof [F5], so the initial bounded-weight premise suffices for its characteristic and support conclusions. By [F2] the limit is signwise stationary. If it were not a minimizer, [F3] would force to be negative somewhere for one sign . Fix that sign, put , and write for the time-dependent risk field of the same original flow. Choose a negative regular level and an interior point of ; continuity at a negative point and density of regular values allow . By [F6] choose a larger negative outer trap containing , and close-forcing radius , so that there. In the unbounded case choose the outer level regular for both finite and limiting sphere functions, as provided by [F6]. Reduce the forcing radius if needed to match the escape neighborhood in [F4] for the selected sign and inner set .
By [F2], the projections eventually remain in that BL neighborhood and the forcing eventually remains within of . Fix such a time first. Only now use [F5] to choose bounding the weights of the entire support at . Use the proof coordinate ; the model and its feature moment remain unchanged. The constant-penalty conclusion in [F6] gives and . On a characteristic with and , the original flow equations become Consequently, when this angular path is traced backward, it satisfies the favorable equation from [F6] with the positive continuous scale and reversed but still close forcing. The universal reversed-forcing trap in [F6] keeps it in and hence keeps . Now fix and suppose a support point over has . Trace its characteristic backward on the maximal interval ending at on which . The reversed trap is first applied on compact subintervals where ; closedness of and continuity extend membership to a possible zero endpoint. If it starts at a time with , integrating from to contradicts . If it reaches , then , also a contradiction. Thus no support point over has after . By [F5], every support point has a characteristic representative, so this holds for the full support.
Fix . By [F5] the vertical line over meets , and by step 2.1 one such point has . If , the strict inequality puts this support point in the interior of the favorable cone, so an open neighborhood of it has positive measure. If , write . The scalar potential in the isometric coordinate is . Since , its minimum-norm subgradient at gives , and the angular velocity is zero. In a small spacetime neighborhood of this point, the favorable equation on has , while the opposite equation on has . Continuity of the characteristic and its integral equation therefore give for every sufficiently small , while remains strictly inside . Its image is still a support point by [F5], so again there is positive favorable-cone mass. In either case [F4] forces a later exit from the neighborhood in which all projections after remain. This contradiction proves global optimality for the original flow, with the original feature moment unchanged.
The negative-branch contradiction in steps 1.1–3.1 uses only the existence of a negative value of , signwise stationarity and the given flow; hence both and are nonnegative everywhere. Let be the Jordan-sign lift from [F1], supported on the sections and . It is a finite nonnegative Borel measure, has feature moment , and satisfies . Its first variation at an arbitrary is , equal to for , for , and for . It is therefore nonnegative everywhere. By signwise stationarity [F2], it vanishes -almost everywhere. Thus [F8] makes a global minimizer of over all finite nonnegative Borel measures, including finite-energy competitors without a finite moment.
By [F7], , and step 3.1 gives . Step 4.1 gives on the unrestricted finite-nonnegative-measure domain. Consequently , as asserted.
Source note
The forcing neighborhood is chosen before the late time and its weight bound ; the subsequent clearance wait does not alter that neighborhood. The proof establishes the actual dynamical penalty limit separately. Static equality of signed projections alone still does not determine lifted penalties.
Depends on
- Feature-measure energy and its regularity assumptions
- Wasserstein gradient flow for a feature-measure energy
- Partially one-homogeneous signed measure projection
- Relaxed objective for a partially one-homogeneous projection
- The Axiom of Choice
- Stationarity of a partially homogeneous signed projection limit
- Signwise first-variation conditions minimize the relaxed objective
- Nonoptimal partial projections have an escaping sign branch
- Partial-homogeneity flows preserve box separation
- Signed narrow convergence controls variation tails and Bochner moments
- The full global partial-model hypotheses force a constant penalty coefficient
- Negative partial-homogeneity levels trap all sufficiently close forcings
- A partial signed projection limit has convergent penalty and energy
- One-sided semiconvex characteristics give W2 uniqueness and stability
- The global Wasserstein gradient flow exists uniquely and is stable
- First variation and optimality over nonnegative measures
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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)