Max-Plus Linear Approximations for Deterministic Continuous-State Markov Decision Processes

In this paper, we explore classical concepts in linear representations in machine learning and signal processing, namely approximations from a finite basis and sparse

Chapter 4 defines polyadic approximations for both the λ-calculus of intuitionistic linear logic and MELL proof-nets, in different approximation flavors.. Chapter 5 states and

Moreover, the results on reachability analysis of uMPL systems are used to solve the conditional reachability problem, which is closely related to the support calculation of

In this section we obtain Fr´echet lower bound by a greedy algorithm based on max-plus linearity of the Fr´echet problem and the well-known Monge property (see e.g.. the detailed

The main interest in these results is to assert that we can always K -majorize the state vectors of a linear max-plus system through the construction of K -monotone bounds of the

As in conventional linear systems theory [10], the control is synthe- sized in order to keep the system state x in the kernel of the output matrix C.. Section 2 recalls some

work of Katz (2007) that treated the problem under the light of geometrical control theory, providing sufficient condition to solve a class of problems.. This work was a

As a main result, it is shown that the optimal solution of this observer- based control problem leads to a greater control input than the one obtained with the output