BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:GAMA: QUANTUM AND QUANTUM-INSPIRED ALGORITHMS - Sridhar Tayur\, Pr
 ofessor of Operations Management\, Tepper School of Business\, Carnegie Me
 llon University
DTSTART:20200109T123000Z
DTEND:20200109T140000Z
UID:TALK136345@talks.cam.ac.uk
CONTACT:Emily Brown
DESCRIPTION:I will discuss two original approaches to solve nonlinear inte
 ger optimisation problems that arise in applications in finance\, cancer g
 enomics and supply chain optimisation. Our Graver Augmented Multiseed Algo
 rithm (GAMA) utilises augmentation along Graver basis elements (the improv
 ement direction is obtained by comparing objective function values) from t
 hese multiple initial feasible solutions.\n\n* A hybrid quantum classical 
 approach (GAMMA-Q) that have the potential to solve a variety of hard line
 ar and nonlinear integer programs\, as the form a test set (optimality cer
 ficate). We test two hybrid quantum classical algorithms (on D-Wave) one f
 or computing Graver basis and a second for optimising nonlinear integer pr
 ograms that utilise GRacer bases to understand the stengths and limitation
 s of the practical quantum annealers available today. Our experiments sugg
 est that with a modest increase in coupler precision along with near term 
 improvements in the number of qubits and connectivity that are expected th
 e ability to outperform classical best in class algorithms is within reach
 .\n\n* A (fully classical) approach (GAMA-C) to solving certain non convex
  integer programs. This\nmethod is well suited for Cardinality Boolean Qua
 dratic Problems (CBQP)\, Quadratic Semi Assignment Problems (QSAP) and Qua
 dratic Assignment Problems (QAP). Sensitivity analysis\nindicates that the
  rate at which GAMA slows down as the problem size increases is much lower
 \nthan that of Gurobi. We find that for several instances of practical rel
 evance\, GAMA vastly outperforms in terms of time to find the optimal solu
 tion (by two or three orders of magnitude).\n\n* Results of applying GAMA 
 on data from The Cancer Genome Project (TCGA) to fi nd mutated\ndriver pat
 hways are encouraging. I will discuss some results on Acute Myleoid Luekem
 ia (AML)\nand Glioblastoma Multiforme (GBM).\n\n
LOCATION:LT4\, Simon Sainsburys Building\, Cambridge Judge Business School
END:VEVENT
END:VCALENDAR