Recently, Associate Professor Zhang Shuaiqi from the School of Mathematics, in collaboration with Professor Chen Zhenqing from the University of Washington, published two research papers, “Stochastic maximum principle for sub-diffusions and its application” and “Stochastic maximum principle for fully coupled forward-backward stochastic differential equations driven by subdiffusion,” in the SIAM Journal on Control and Optimization (SICON), one of the top three journals in the field of control. These studies mark the first proposal and solution to the control problem of stochastic systems driven by anomalous subdiffusion, investigating stochastic differential equations (SDEs) and fully coupled forward-backward stochastic differential equations (FBSDEs) governed by subdiffusion processes.

Anomalous subdiffusion, a stochastic process slower than Brownian motion, exhibits unique "active" and "stagnant" characteristics. Zhang Shuaiqi and collaborators highlighted that these traits better reflect scenarios of inactive market trading, making subdiffusion more practical for modeling stock prices in sluggish markets. Since the system combines deterministic and stochastic elements, its control problem involves hybrid control integrating both approaches—a novel innovation distinguishing it from existing literature.

Founded in 1966, the SIAM Journal on Control and Optimization covers fields such as mathematics and applied sciences, advancing control theory, optimization methods, and interdisciplinary applications while providing critical theoretical support for engineering, economics, biology, and beyond. Research on anomalous subdiffusion remains scarce, and reviewers praised the papers as “noteworthy” and “impressive.” This groundbreaking achievement in mathematics and control will bolster CUMT’s digital transformation efforts, enabling deeper integration of control theory with big data and AI technologies to drive breakthroughs in complex system optimization, intelligent decision-making, and related fields.