On characterizing optimal learning trajectories in a class of learning problems

• URL: http://arxiv.org/abs/2501.16521v2
• Date: Thu, 06 Feb 2025 14:54:13 GMT
• Title: On characterizing optimal learning trajectories in a class of learning problems
• Authors: Getachew K Befekadu,
• Abstract summary: This paper exploits the relationship between the maximum principle and dynamic programming for characterizing optimal learning trajectories in a class of learning problem. We provide an algorithmic recipe how to construct the corresponding optimal learning trajectories leading to the optimal estimated model parameters for such a class of learning problem.
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• Abstract: In this brief paper, we provide a mathematical framework that exploits the relationship between the maximum principle and dynamic programming for characterizing optimal learning trajectories in a class of learning problem, which is related to point estimations for modeling of high-dimensional nonlinear functions. Here, such characterization for the optimal learning trajectories is associated with the solution of an optimal control problem for a weakly-controlled gradient system with small parameters, whose time-evolution is guided by a model training dataset and its perturbed version, while the optimization problem consists of a cost functional that summarizes how to gauge the quality/performance of the estimated model parameters at a certain fixed final time w.r.t. a model validating dataset. Moreover, using a successive Galerkin approximation method, we provide an algorithmic recipe how to construct the corresponding optimal learning trajectories leading to the optimal estimated model parameters for such a class of learning problem.

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