Program Synthesis for Non-Linear Real Arithmetic: Going Beyond Realizability
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918519424155648 |
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| author | Akshay, S. Chakraborty, Supratik Govind, R. Joshi, Aniruddha R. |
| author_facet | Akshay, S. Chakraborty, Supratik Govind, R. Joshi, Aniruddha R. |
| contents | We study the problem of synthesizing programs from nonlinear real arithmetic (NRA) specifications. Existing techniques, such as syntax-guided synthesis (SyGuS), fail to synthesize programs when the specification is unrealizable. We argue this is unsatisfactory in many situations, and aim to synthesize programs from arbitrary NRA specifications, such that for any input, the synthesized program either produces outputs satisfying the specification or reports non-existence of any such output. To avoid rounding errors inherent in floating-point arithmetic, we restrict our programs to work on rational inputs and outputs.
We first show that our variant of the synthesis problem is as hard as a long-standing open problem in number theory, and that synthesizing loop-free programs from arbitrary NRA specifications with rational inputs and outputs is impossible in general. Second, we present a sound and complete synthesis algorithm for the case where the specification involves a single output variable. We also show that for realizable specifications, a program generated by SyGuS for NRA (real inputs and outputs) serves as a solution to our problem, where inputs and outputs are rationals. Third, we provide a sound (but necessarily incomplete) synthesis algorithm for the general case of specifications. We have implemented our approach in a prototype tool called NQSynth that solves many benchmarks beyond the reach of state-of-the-art SyGuS tools, even when we render the specifications realizable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24263 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Program Synthesis for Non-Linear Real Arithmetic: Going Beyond Realizability Akshay, S. Chakraborty, Supratik Govind, R. Joshi, Aniruddha R. Programming Languages Logic in Computer Science We study the problem of synthesizing programs from nonlinear real arithmetic (NRA) specifications. Existing techniques, such as syntax-guided synthesis (SyGuS), fail to synthesize programs when the specification is unrealizable. We argue this is unsatisfactory in many situations, and aim to synthesize programs from arbitrary NRA specifications, such that for any input, the synthesized program either produces outputs satisfying the specification or reports non-existence of any such output. To avoid rounding errors inherent in floating-point arithmetic, we restrict our programs to work on rational inputs and outputs. We first show that our variant of the synthesis problem is as hard as a long-standing open problem in number theory, and that synthesizing loop-free programs from arbitrary NRA specifications with rational inputs and outputs is impossible in general. Second, we present a sound and complete synthesis algorithm for the case where the specification involves a single output variable. We also show that for realizable specifications, a program generated by SyGuS for NRA (real inputs and outputs) serves as a solution to our problem, where inputs and outputs are rationals. Third, we provide a sound (but necessarily incomplete) synthesis algorithm for the general case of specifications. We have implemented our approach in a prototype tool called NQSynth that solves many benchmarks beyond the reach of state-of-the-art SyGuS tools, even when we render the specifications realizable. |
| title | Program Synthesis for Non-Linear Real Arithmetic: Going Beyond Realizability |
| topic | Programming Languages Logic in Computer Science |
| url | https://arxiv.org/abs/2605.24263 |