Any python data structure could be seen as a big tree of related objects. Would it be useful for ... more Any python data structure could be seen as a big tree of related objects. Would it be useful for you to recognize in this big tree some specific form? Recognizing of subtrees is really a common aim in compilers implementation. But it's not an easy task. I recently found that I could easily use behavior trees to do such things. However this kind of techniques could be useful in other situation. This talk want to popularize this technique by explaining how behavior trees work in common way, how to adapt then for a bottom-up tree matching algorithm. For instance, some HTML parser provide CSS Selector function to match one node at the time optionally related to some ancestor. Here, with this kind of matching algorithm you could match many nodes in only one pass. I create on my github a "treematching" module that use this algorithm to provide an Embedded DSL to write patterns, match any python data structure and in some case, allow you to do tree rewriting.
We have made experiments with reordering algorithms on sparse very large graphs (VLGs), we have c... more We have made experiments with reordering algorithms on sparse very large graphs (VLGs), we have considered only undirected, unweighted, sparse and huge graphs, i.e. G = (V;E) with n = jV j from million to billion of nodes and with jEj = O(jV j). The problem of reordering a matrix to enhance the computation time (and sometimes the memory) is traditional in numerical algorithms but we focus on this short paper on results obtained for the approximate computation of the diameter of a sparse VLG (with some graphs on various different computers). The problem of reordering a graph has already been pointed, explicitly or implicitly by a lot of people, from the numerical community but also from the graph community, like the authors of the Louvain algorithm when they write that choosing an order is thus worth studying since it could accelerate the Louvain algorithm. Our experimental results show clearly that it can be worth (and simple) to preprocess a sparse VLG with a reordering algorithm.
Any python data structure could be seen as a big tree of related objects. Would it be useful for ... more Any python data structure could be seen as a big tree of related objects. Would it be useful for you to recognize in this big tree some specific form? Recognizing of subtrees is really a common aim in compilers implementation. But it's not an easy task. I recently found that I could easily use behavior trees to do such things. However this kind of techniques could be useful in other situation. This talk want to popularize this technique by explaining how behavior trees work in common way, how to adapt then for a bottom-up tree matching algorithm. For instance, some HTML parser provide CSS Selector function to match one node at the time optionally related to some ancestor. Here, with this kind of matching algorithm you could match many nodes in only one pass. I create on my github a "treematching" module that use this algorithm to provide an Embedded DSL to write patterns, match any python data structure and in some case, allow you to do tree rewriting.
We have made experiments with reordering algorithms on sparse very large graphs (VLGs), we have c... more We have made experiments with reordering algorithms on sparse very large graphs (VLGs), we have considered only undirected, unweighted, sparse and huge graphs, i.e. G = (V;E) with n = jV j from million to billion of nodes and with jEj = O(jV j). The problem of reordering a matrix to enhance the computation time (and sometimes the memory) is traditional in numerical algorithms but we focus on this short paper on results obtained for the approximate computation of the diameter of a sparse VLG (with some graphs on various different computers). The problem of reordering a graph has already been pointed, explicitly or implicitly by a lot of people, from the numerical community but also from the graph community, like the authors of the Louvain algorithm when they write that choosing an order is thus worth studying since it could accelerate the Louvain algorithm. Our experimental results show clearly that it can be worth (and simple) to preprocess a sparse VLG with a reordering algorithm.
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Papers by Lionel Auroux