Certifying Global Optimality of Stationary Points in Imperfect-Recall Games
Abstract
Single-player decision problems with imperfect recall model a player who may forget information she once had. They arise wherever a strategy must stay compact: the absent-minded driver, imperfect-recall abstractions of poker, and team games compressed into a single forgetful player. The ex-ante optimal behavior strategy of such a problem maximizes a polynomial over a product of simplices. Deciding optimality is -complete, near-optimality is NP-hard, and the degree of the polynomial grows with the number of visits to an information set. Practice therefore relies on first-order methods: regret matching and its predictive-plus variant reach stationary points at scale. Such a point comes with no guarantee about its distance from the global optimum. We add a certificate layer that runs after the solver. A support-pruned sum-of-squares feasibility program at level bounds how far any strategy can beat the reported value. A posterior step turns the floating-point solution into a bound re-verified in exact and interval arithmetic, and the same certificate decomposes the residual gap across information sets. On instances regenerated from the generators of the reference benchmark, the layer certifies the PRM stationary point as globally optimal within relative tolerance on , or . The verdict is invariant across seed groups and of first-order methods. The global solver SCIP corroborates it to the same tolerance on all ; on one instance SCIP reaches that tolerance only at its one-hour limit, where the layer needs seconds. Support pruning is lossless at level one and wherever strict complementarity holds. The one certified loss is an interior cubic whose gap narrows one level up. On these instances, the stationary points of the standard solvers are almost always global optima, and a sum-of-squares certificate makes that fact checkable in seconds and provable in exact arithmetic in minutes.
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