Truncated Differentiation Through Primal-Dual Solvers for Inverse Potential Mean-Field Games

Abstract

We study inverse potential mean-field games (MFGs), in which an unknown spatial inverse-cost (mobility) map is inferred from observed population densities. We solve the forward MFG with a preconditioned primal-dual hybrid gradient (PDHG) method and develop Jacobian-free backpropagation (JFB-$r$), which records only the final $r$ iterations from a detached warm start while retaining the full forward solve. To analyze this truncated differentiation method, we show that the exact-proximal dual-extrapolated PDHG map is a metric resolvent of the maximal monotone KKT operator. This resolvent view shows that JFB-$r$ exactly differentiates a finite-trajectory surrogate and, under a locally fixed active set at an exact equilibrium detach point, converges to the implicit gradient as the tracked depth increases. Across several inverse-MFG settings, numerical experiments show that JFB-r at moderate tracked depths can achieve recovery accuracy comparable to full unrolling while reducing memory and runtime.

Disclosure

“ve depth, generalized kink sensitivity, and extending the framework to higher-dimensional inverse problems arising in control and dynamics [35, 17], as well as inverse transport and flow reconstruction [59, 48, 24, 5]. Acknowledgments. ChatGPT (OpenAI) was used to polish the authors’ written text for spelling, grammar, and style. Claude (Anthropic) was used to assist with JAX-based accel- eration of portions of the numerical implementation, specifically translating and vectorizi”

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2026-08-23T19:19:18.521703Z

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