@inproceedings{497,
abstract = {One central issue in the formal design and analysis of reactive systems is the notion of refinement that asks whether all behaviors of the implementation is allowed by the specification. The local interpretation of behavior leads to the notion of simulation. Alternating transition systems (ATSs) provide a general model for composite reactive systems, and the simulation relation for ATSs is known as alternating simulation. The simulation relation for fair transition systems is called fair simulation. In this work our main contributions are as follows: (1) We present an improved algorithm for fair simulation with Büchi fairness constraints; our algorithm requires O(n 3·m) time as compared to the previous known O(n 6)-time algorithm, where n is the number of states and m is the number of transitions. (2) We present a game based algorithm for alternating simulation that requires O(m2)-time as compared to the previous known O((n·m)2)-time algorithm, where n is the number of states and m is the size of transition relation. (3) We present an iterative algorithm for alternating simulation that matches the time complexity of the game based algorithm, but is more space efficient than the game based algorithm. © Krishnendu Chatterjee, Siddhesh Chaubal, and Pritish Kamath.},
author = {Chatterjee, Krishnendu and Chaubal, Siddhesh and Kamath, Pritish},
location = {Fontainebleau, France},
pages = {167 -- 182},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
title = {{Faster algorithms for alternating refinement relations}},
doi = {10.4230/LIPIcs.CSL.2012.167},
volume = {16},
year = {2012},
}
@article{506,
author = {Sixt, Michael K},
journal = {Journal of Cell Biology},
number = {3},
pages = {347 -- 349},
publisher = {Rockefeller University Press},
title = {{Cell migration: Fibroblasts find a new way to get ahead}},
doi = {10.1083/jcb.201204039},
volume = {197},
year = {2012},
}
@misc{5378,
abstract = {One central issue in the formal design and analysis of reactive systems is the notion of refinement that asks whether all behaviors of the implementation is allowed by the specification. The local interpretation of behavior leads to the notion of simulation. Alternating transition systems (ATSs) provide a general model for composite reactive systems, and the simulation relation for ATSs is known as alternating simulation. The simulation relation for fair transition systems is called fair simulation. In this work our main contributions are as follows: (1) We present an improved algorithm for fair simulation with Büchi fairness constraints; our algorithm requires O(n3 · m) time as compared to the previous known O(n6)-time algorithm, where n is the number of states and m is the number of transitions. (2) We present a game based algorithm for alternating simulation that requires O(m2)-time as compared to the previous known O((n · m)2)-time algorithm, where n is the number of states and m is the size of transition relation. (3) We present an iterative algorithm for alternating simulation that matches the time complexity of the game based algorithm, but is more space efficient than the game based algorithm.},
author = {Chatterjee, Krishnendu and Chaubal, Siddhesh and Kamath, Pritish},
issn = {2664-1690},
pages = {21},
publisher = {IST Austria},
title = {{Faster algorithms for alternating refinement relations}},
doi = {10.15479/AT:IST-2012-0001},
year = {2012},
}
@article{6588,
abstract = {First we note that the best polynomial approximation to vertical bar x vertical bar on the set, which consists of an interval on the positive half-axis and a point on the negative half-axis, can be given by means of the classical Chebyshev polynomials. Then we explore the cases when a solution of the related problem on two intervals can be given in elementary functions.},
author = {Pausinger, Florian},
issn = {1812-9471},
journal = {Journal of Mathematical Physics, Analysis, Geometry},
number = {1},
pages = {63--78},
publisher = {B. Verkin Institute for Low Temperature Physics and Engineering},
title = {{Elementary solutions of the Bernstein problem on two intervals}},
volume = {8},
year = {2012},
}
@misc{5379,
abstract = {Computing the winning set for Büchi objectives in alternating games on graphs is a central problem in computer aided verification with a large number of applications. The long standing best known upper bound for solving the problem is ̃O(n·m), where n is the number of vertices and m is the number of edges in the graph. We are the first to break the ̃O(n·m) boundary by presenting a new technique that reduces the running time to O(n2). This bound also leads to O(n2) time algorithms for computing the set of almost-sure winning vertices for Büchi objectives (1) in alternating games with probabilistic transitions (improving an earlier bound of O(n·m)), (2) in concurrent graph games with constant actions (improving an earlier bound of O(n3)), and (3) in Markov decision processes (improving for m > n4/3 an earlier bound of O(min(m1.5, m·n2/3)). We also show that the same technique can be used to compute the maximal end-component decomposition of a graph in time O(n2), which is an improvement over earlier bounds for m > n4/3. Finally, we show how to maintain the winning set for Büchi objectives in alternating games under a sequence of edge insertions or a sequence of edge deletions in O(n) amortized time per operation. This is the first dynamic algorithm for this problem.},
author = {Chatterjee, Krishnendu and Henzinger, Monika},
issn = {2664-1690},
pages = {20},
publisher = {IST Austria},
title = {{An O(n2) time algorithm for alternating Büchi games}},
doi = {10.15479/AT:IST-2011-0009},
year = {2011},
}