How can an agent generalize its knowledge to new circumstances? To learn
effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. Thi ...
Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO alg ...
Many important application problems in engineering can be formalized as nonlinear
optimization tasks. However, numerical methods for solving such problems
are brittle and do not scale well. For example, these methods d ...
We investigate the optimal harvesting strategies for McKendrick type population models. Models of this type endow the population with a continuous age structure. They consist of a partial differential equation with a bou ...
Many different types of distributed batch scheduling systems have been developed
in the last decade to take advantage of the decentralization of computers and the
enormous investments that many companies and educationa ...
A simplification of the proof of the maximum principle of
Pontryagin is obtained for constrained and unconstrained optimal control
problems. Two numerical methods for solving optimal control problems
with guaranteed e ...