Functional Gradient Descent with Adaptive Representations
Functional optimization problems are typically solved by optimizing the parameters of a fixed representation, such as a neural network, resulting in highly nonconvex losses that complicate both training and theoretical analysis. An interesting alternative is functional gradient descent (FGD), that is, gradient descent directly in function space, which benefits from strong convergence results and admits a clean theory. However, FGD is difficult to implement in practice because functional gradients are infinite-dimensional, and thus cannot be fully computed nor stored in memory. Existing implementations therefore rely on fixed approximations, which introduce approximation error. We propose ...
| Authors | Daniel Csillag, Rodrigo Schuller, Pedro Dall'Antonia et al. |
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| Primary category | math.OC |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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