arXiv subjects

Jingni Yang

Publications and source records attributed to Jingni Yang.

3 recordsLinked to original sources

arXiv2026

Faithful Decoding

This paper studies transformations that increase efficiency in solving equilibrium systems without information loss. Our approach exploits order-theoretic structure commonly found in economic problems to obtain conditions under which high-dimensional systems can be transformed into low-dimensional systems while preserving exact relationships between their solutions. The transformations can also be used for purposes other than dimensionality reduction, such as simplifying analysis and facilitating stochastic approximation routines. The theoretical ideas are illustrated using applications from economics and finance. In a real option problem, we demonstrate speed gains of up to 70,000 times.

econ.GN↗

arXiv2026

Abstract Dynamic Programming on Partially Ordered Spaces

We study abstract dynamic programs on partially ordered spaces, pairing the order-theoretic approach to dynamic programming with topological and metric foundations. We show that readily verifiable forms of topological stability, such as global stability and contractivity of the policy operators, deliver the fundamental optimality properties of dynamic programming together with convergence of value function iteration, Howard policy iteration, and optimistic policy iteration. We also prove that stationary policies dominate nonstationary policy plans under very weak assumptions. Applications include Markov decision processes, structural estimation problems in which maximization and integration are interchanged, optimal stopping without discounting, and Bayesian sequential analysis. For the last two, our results weaken existing assumptions and extend algorithmic guarantees for foundational problems.

math.OC↗

arXiv2025

Dynamic Programming: From Local Optimality to Global Optimality

In the theory of dynamic programming, an optimal policy is a policy whose lifetime value dominates that of all other policies from every possible initial condition in the state space. This raises a natural question: when does optimality from a single state imply optimality from every state? Working in a general setting, we provide sufficient conditions for this property that relate to reachability and irreducibility. Our results have significant implications for modern policy-based algorithms used to solve large-scale dynamic programs. We illustrate our findings by applying them to an optimal savings problem via an algorithm that implements gradient ascent in a policy space constructed from neural networks.

math.OC↗