UBC Computer Science Assistant Professor Mark Schmidt, together with co-authors Nicolas Le Roux and Francis Bach, received the prestigious Lagrange Prize in Continuous Optimization, for their paper "Minimizing finite sums with the stochastic average gradient" (NIPS, 2012; Mathematical Programming, 2017).
The prize is awarded by the Mathematical Optimization Society and SIAM every three years to recognize outstanding contribution to the area of continuous optimization. The award is evaluated based on the work's mathematcial quality, significance, and originality.
Here is a citation for the paper:
“This paper is the first in a series of significant advances in the design and analysis of stochastic gradient methods applied to finite-sum problems. In particular, it establishes that in this setting, the proposed variant of a stochastic gradient method achieves a linear convergence rate. This novel approach has resulted in a surge of interest in variance reduction methods that achieve superior performance compared to other first-order methods. A wide range of applications benefit from this methodology, including linear least squares, principal component analysis, and L1-regularization problems. In summary, this work represents a paradigm shift in theoretical analysis of stochastic methods applied to finite-sum optimization problems.”