The Theory and Practice of MAP Inference over Non-Convex Constraints

• URL: http://arxiv.org/abs/2602.08681v2
• Date: Tue, 10 Feb 2026 08:49:02 GMT
• Title: The Theory and Practice of MAP Inference over Non-Convex Constraints
• Authors: Leander Kurscheidt, Gabriele Masina, Roberto Sebastiani, Antonio Vergari,
• Abstract summary: In safety-critical settings, probabilistic ML systems have to make predictions subject to algebraic constraints.<n>This makes computing this constrained maximum a posteriori (MAP) prediction efficiently and reliably extremely challenging.<n>We devise a scalable message-passing algorithm for this tractable fragment.<n>Then, we devise a general constrained MAP strategy that interleaves partitioning the domain into convex feasible regions.
• Score: 11.058494098615576
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In many safety-critical settings, probabilistic ML systems have to make predictions subject to algebraic constraints, e.g., predicting the most likely trajectory that does not cross obstacles. These real-world constraints are rarely convex, nor the densities considered are (log-)concave. This makes computing this constrained maximum a posteriori (MAP) prediction efficiently and reliably extremely challenging. In this paper, we first investigate under which conditions we can perform constrained MAP inference over continuous variables exactly and efficiently and devise a scalable message-passing algorithm for this tractable fragment. Then, we devise a general constrained MAP strategy that interleaves partitioning the domain into convex feasible regions with numerical constrained optimization. We evaluate both methods on synthetic and real-world benchmarks, showing our approaches outperform constraint-agnostic baselines, and scale to complex densities intractable for SoTA exact solvers.

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