The following technical report is available from
http://aib.informatik.rwth-aachen.de:
Computing Game Metrics on Markov Decision Processes
Hongfei Fu
AIB 2012-08
In this paper we study the complexity of computing the game bisimulation
metric defined by de Alfaro et al. on Markov Decision Processes. It is
proved by de Alfaro et al. that the undiscounted version of the metric
is characterized by a quantitative game \mu-calculus defined by de
Alfaro and Majumdar, which can express reachability and \omega-regular
specifications. And by Chatterjee et al. that the discounted version of
the metric is characterized by the discounted quantitative game
\mu-calculus. In the discounted case, we show that the metric can be
computed exactly by extending the method for Labelled Markov Chains by
Chen et al. And in the undiscounted case, we prove that the problem
whether the metric between two states is under a given threshold can be
decided in NP and coNP, which improves the previous PSPACE upperbound by
Chatterjee et al.
The following technical report is available from
http://aib.informatik.rwth-aachen.de:
dco/c++ - Derivative Code by Overloading in C++
Johannes Lotz, Klaus Leppkes and Uwe Naumann
AIB 2011-06
Algorithmic Differentiation is a widely used technique for computing
derivatives of implemented mathematical functions. In this technical
report we want to introduce the tool dco/c++ written by the authors
implementing Algorithmic Differentiation by Overloading for C/C++. After
a general picture of the capabilities dco/c++ provides, we show its
performance with emphasis on adjoint computations in comparison to four
other freely available overloading tools.