My research deals
with applications of optimization and matrix theory
to algorithmic development for both
continuous and discrete
optimization
problems.
My research interests include:
optimization in finite
dimensional and
abstract spaces; linear, nonlinear and semidefinite programming; matrix
eigenvalue
problems; and numerical analysis of algorithms.

My combinatorial optimization
work applies convex relaxations to hard combinatorial
optimization problems. The relaxations are based on Lagrangian duality,
and in many cases they result in Semidefinite Programming relaxations.

I am a team member for the
MITACS
Project
on High Performance Optimization: Theory, Algorithm Design, and
Engineering Applications