We propose a modified sequential quadratic programming (SQP) method for solving mixed-integer nonlinear programming problems. Under the assumption that integer variables have a s...
The kernel function plays a central role in kernel methods. In this paper, we consider the automated learning of the kernel matrix over a convex combination of pre-specified kerne...
Interior point methods (IPMs) have proven to be an efficient way of solving quadratic programming problems in predictive control. A linear system of equations needs to be solved in...
Amir Shahzad, Eric C. Kerrigan, George A. Constant...
Developing scalable algorithms for solving partially observable Markov decision processes (POMDPs) is an important challenge. One promising approach is based on representing POMDP...
Christopher Amato, Daniel S. Bernstein, Shlomo Zil...
Iterative optimization algorithms such as the forward-backward and Douglas-Rachford algorithms have recently gained much popularity since they provide efficient solutions to a wi...