{"id":156951,"date":"2009-01-29T00:00:00","date_gmt":"2009-01-29T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/linear-functional-fixed-points\/"},"modified":"2018-10-16T21:46:48","modified_gmt":"2018-10-17T04:46:48","slug":"linear-functional-fixed-points","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/linear-functional-fixed-points\/","title":{"rendered":"Linear Functional Fixed-points"},"content":{"rendered":"
\n

We introduce a logic of functional fixed-points. It is suitable for analyzing heap-manipulating programs and can encode several of the logics recently introduced for reasoning with transitive\u00a0 closures. While full fixed-point logic remains undecidable, several subsets admit decision procedures. In particular, for the logic of linear functional fixed-points, we develop an abstraction refinement integration of the SMT solver Z3 and a satisfiability checker for propositional linear-time temporal logic. The integration refines the temporal abstraction by generating safety formulas until the temporal abstraction is unsatisfiable or a model for it is also a model for the functional fixed-point formula.<\/p>\n<\/div>\n

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We introduce a logic of functional fixed-points. It is suitable for analyzing heap-manipulating programs and can encode several of the logics recently introduced for reasoning with transitive\u00a0 closures. While full fixed-point logic remains undecidable, several subsets admit decision procedures. In particular, for the logic of linear functional fixed-points, we develop an abstraction refinement integration of 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