On November 5, 2016, Prof. He Bingsheng of SUSTech Department of Mathematics received the Outstanding Contribution Award of the First Jiangsu Province Industrial and Applied Mathematics Awards from the Jiangsu Provincial Society for Industrial and Applied Mathematics. Prof. Wang Yuanming of Southeast University, who founded the Jiangsu Provincial Society for Industrial and Applied Mathematics, also won the award.

At the awarding ceremony, Prof. He Bingsheng delivered a special report entitled “The Splitting Contraction Algorithm from a Blind Mountain Climber to the Convex Optimization.” The report, rich in content and easy to understand, was welcomed by attendees.

The optimization method is a practical subject of applied mathematics. Feng Kang, a famous mathematician, once said, “A scientist’s greatest ability is to cut through complexity and solve complex problems in simple methods.” Following the principle of constantly pursuing simplicity and unity in the research of optimal theories and methods, Prof. He Bingsheng has been providing problem-solving tools for relevant disciplines. His research findings are widely applied in machine learning, image and video processing, and other information technology fields .

His award-winning achievements cover four aspects: the projection contraction algorithm; the adjustment and comparison criteria for selection of parameters; the globally convergent splitting algorithm designed under the unified framework of the convex optimization splitting contraction algorithm (This algorithm was described an “a simple scheme that often works well” by Stephen Boyd, a professor with Stanford University and an academician of America’s National Academy of Engineering, in his review article. Prof. He Bingsheng proved the alternating direction method’s convergence rate, and the work was published in the SIAM Journal on Numerical Analysis. Four years later, the paper was ranked 2nd among the journal’s 20 most popular papers. And Prof. Stephen Boyd of Stanford University listed the paper as one of the few main theoretical literatures in the field of this algorithm on relevant professional websites); the relaxation proximal-point algorithm constructed under the framework of variational inequality and the corresponding condition of convergence (Antonin Chambolle, a famous scholar of image processing, said that it “greatly simplifies the convergence analysis” and called it an “elegant interpretation,” and the findings have been recognized by more and more scholars in recent years).

As is learned, in late October, the Jiangsu Provincial Society for Industrial and Applied Mathematics invited Academician Li Daqian, former President of the Chinese Society for Industrial and Applied Mathematics, and other experts to form the panel of judges, which selected the award winners after two rounds of voting.