Prof.Chandrajit Bajaj, gave a lecture titled “Scalable Geometric Optimization with Applications to Molecular Interactions” at AMSS on 13, December 2018.
Geometric optimization is the computational reduction technique of choice for a wide variety of model selection, ranking and assembly prediction problems. Moreover, optimization occurs naturally for solutions to rigid and flexible geometric shape similarity, complementarity matching problems (e.g. predicting multi-component assemblies, disaster reconstructions etc). The optimization functional is often a multi-dimensional correlation integral while the search space is the product of transformations groups with dimension growth exponential in the number of movable components (e.g. O(3^n) for an n-residue torsionally flexible molecule ). In this talk, He dwelled on solution of geometric optimization methods that combated the curse of high dimensionality, and also achieved adequate tradeoffs between speed and accuracy. Fast approximate estimations to the geometric similarity or complementarity matching optimization problem took advantage of a new scheme of generating low-discrepancy samplings of the n-product configuration spaces, as well as utilization of approximate non-uniform fast Fourier transforms.
Chandrajit Bajaj is a Professor in the Department of Computer Science, and Institute of Computational Engineering and Sciences, and Center for Computational Visualization at The University of Texas at Austin, USA. He received his B.Tech. in Electrical Engineering (1980) from Indian Institute of Technology, New Delhi, India; and his M.S. and Ph.D. in Computer Sciences (1983, 1984) from the Cornell University, Ithaca, USA. He is the Fellow of AAAS, Fellow of ACM, Fellow of IEEE, and Fellow of SIAM.