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Nemytykh},pages = {34--48},series = {{EPTCS}},volume = {299},bibsource = {dblp computer science bibliography, https://dblp.org},biburl = {https://dblp.org/rec/journals/corr/abs-1908-07189.bib},comment = {partial evaluation on Constraint Horn Clauses (CHC) programs},doi = {10.4204/EPTCS.299.6},priority = {prio1},relevance = {relevant},timestamp = {Fri, 27 Dec 2019 21:15:10 +0100},url = {https://doi.org/10.4204/EPTCS.299.6},}<<<<<<< HEAD@InProceedings{DBLP:conf/sas/AliasDFG10,author = {Christophe Alias and Alain Darte and Paul Feautrier and Laure Gonnord},booktitle = {Static Analysis - 17th International Symposium, {SAS} 2010, Perpignan, France, September 14-16, 2010. 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\lipsum
\begin{comment}Base Algorithmfun find_loops_in(P)loop_heads = {}// Dijkstraon loop_back from l' -> l_h:loop_heads := loop_heads + l_hreturn loop_headsendfun heuristic(L: Loop)// same as original paperendfun partial_evaluation(Program P = (PV, L, GT, l_0))let lh := loop_heads(P)let properties := heuristic(P)let initial_state := (l_0, [])let P' := ({l_0}, l_0, {locations: P.l_0, {}) // todofor gt in GT:let gt' = {}for t in gt:(s', l_t') := evaluate(P, s, t)if l_t \in lh:else :gt' := gt' +endendClaims:Every path in P has an equivalent path in the evaluation graph EGEvery path in EG has an equivalent path in P'SCC Algorithm:similar to above but stops at SCC boundaries.\end{comment}
\textit{general transitions} A general transition is a set of transitions withthe probabilities adding up to 1 acting as a single transition for thenon-determinism. Non-determinism can only occur between general transitions andit is resolved first before probabilities are resolved.
\textit{general transitions}: they are sets of (conventional) transitions withthe probabilities adding up to 1, acting as a single transition for theresolution of non-determinism. Non-determinism can only occur between generaltransitions and it is resolved first before probabilities are resolved.
deterministic and non-probabilistic programs are a special case ofprobabilistic programs, where very general transition consist of a singletransition and all general transitions are mutually exclusive.
Deterministic and non-probabilistic programs are a special case of probabilisticprograms, where every general transition consist of a single transition and allgeneral transitions are mutually exclusive.
Let's get to the formal definitions. We will follow the definition of F. Meyer\cite{https://link.springer.com/chapter/10.1007/978-3-319-66167-4_8}.
This thesis will closely follow the definitions of F. Meyer \cite{https://link.springer.com/chapter/10.1007/978-3-319-66167-4_8}.For the following the set of all variables is split into the program variables$\PV$ and temporary variables $TV$. Program variables are assigned at the startof a program and can get values assigned during the run of a program, wherastemporary variables are only existentially quantified for every transition.Hence, they do not \enquote{carry over} between states.
In this thesis we will see a log of integer programs, so let's get familiarwith a couple of examples.% TODO: examples
\todo{examples}\begin{figure}\label{fig:pip1}\caption{A simple probabilistic integer program.}\end{figure}\fref{fig:pip1} gives an example for a very simple probabilistic integerprogram.\todo{give an example for temporary variables}Unsurprisingly, a PIP is executed starting at the start location with a givenassignment $\sigma_0 : \PV \rightarrow \N$. A pair of location and assignment$(l, \sigma) \in \L \times \Sigma$ is called a configuration and representsspecific state of a program during an execution.
configuration is the starting configuration. Note that at this point it isunclear what a validOne can take two vies to the evaluation ofLet's define the semantics of propabilistic integer programs. We will take twoviews onto the semantics of integer programs. First the intuitive one and seconda more formal definition.
configuration is the starting configuration.
During this thesis we will transform programs. The goal is to find equivalentprograms that are easier to analyize by Koat2. But what does equivalence mean?On non probabilistic programs one would expect, that while beeing structurallydifferent, the outcome of the new program is the same as the old one.In addition the new program should take the same time as the old one. Sincecosts are represented as a variable, this is part of the outcome. But theruntime of a program is calculated by ranking-functions and they count thenumber of visits to a location. In order for the runtime to be the same, boundsshall remain the same. \todo{doesn't realy make sence!}
In probabilistic programs, a the run is non-deterministic and hence calculatingthe runtime is impossible. Instead we have expected runtime-bounds. So the newprogram should have the same expected runtime-bounds and the same expectedcosts, as the old program. Let's give a more formal definition.\begin{definition}[Program equivalence]Given two PIP $P = (\V, L, \GT, l_0)$ and $P' = (\V, L', \GT', l_0)$are equivalent if and only if the following properties hold:\begin{enumerate}\item \todo{continue}\end{enumerate}\end{definition}
\subsection{Background}\subsection{Related Works}Koat was first presented in \cite{brockschmidt2014tacas}\begin{comment}\end{comment}
.git.DS_Storebuild*.aux*.bbl*.log