Jingzheng Wu

University of Chinese Academy of Sciences

Papers

Total Citations

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About

Jingzheng Wu is a researcher whose work lies at the intersection of formal methods, optimization, and constraint solving. His key research areas include linear optimization over arithmetic constraints, automated reasoning, and the development of efficient algorithms for decision and optimization problems in computer science. Wu’s most notable contribution is his 2017 paper, "Solving linear optimization over arithmetic constraint formula," which addresses the challenge of optimizing linear objective functions subject to complex arithmetic constraints—a problem with applications in verification, program analysis, and artificial intelligence. While his citation count is modest, with this work accruing 4 citations, the paper represents a foundational step in bridging optimization theory with practical constraint-solving techniques. Wu’s approach offers a novel framework for handling arithmetic constraint formulas, potentially impacting fields like software verification and resource allocation. His research is characterized by a focus on formal rigor and algorithmic efficiency, making it relevant for students and researchers interested in the theoretical underpinnings of optimization and automated reasoning.