Citation for: 2021 Paul Y. Tseng Memorial Lectureship in Continuous Optimization

Yin Zhang, Chinese University of Hong Kong

Yin Zhang has made several deep and transformational theoretical contributions to continuous optimization. Particularly noteworthy are his seminal contributions to interior-point methods for linear, semi-definite and nonlinear programming as well as his more recent contributions to both theory and algorithms for data processing applications including image restoration and compressive sensing. Yin and his co-authors were the first to provide solid theoretical foundation for explaining why primal-dual algorithms are generally faster than either primal or dual algorithms, even though all of them possess the same worst-case complexity order. In addition, Yin was the first to establish polynomial complexity of infeasible interior-point algorithms (a term that he coined). Furthermore, Yin established a new and different approach to problems in compressed sensing and was the first to introduce the splitting and alternating idea to solving image restoration problems. He has developed excellent publicly available software packages supporting his theoretical contributions. Notwithstanding his outstanding theoretical contributions, Yin has a deep commitment to mentoring students, many of them are now active researchers in Asia. He has directed or co-directed 30 graduate students, and is the first recipient of the prestigious Presidential Award for Mentoring at Rice University. Yin's international leadership and continuing contributions in continuous optimization is an inspiration to our community.