8 research outputs found
Violator Spaces: Structure and Algorithms
Sharir and Welzl introduced an abstract framework for optimization problems,
called LP-type problems or also generalized linear programming problems, which
proved useful in algorithm design. We define a new, and as we believe, simpler
and more natural framework: violator spaces, which constitute a proper
generalization of LP-type problems. We show that Clarkson's randomized
algorithms for low-dimensional linear programming work in the context of
violator spaces. For example, in this way we obtain the fastest known algorithm
for the P-matrix generalized linear complementarity problem with a constant
number of blocks. We also give two new characterizations of LP-type problems:
they are equivalent to acyclic violator spaces, as well as to concrete LP-type
problems (informally, the constraints in a concrete LP-type problem are subsets
of a linearly ordered ground set, and the value of a set of constraints is the
minimum of its intersection).Comment: 28 pages, 5 figures, extended abstract was presented at ESA 2006;
author spelling fixe
Simple stochastic games and P-matrix generalized linear complementarity problems
We show that the problem of finding optimal strategies for both players in a simple stochastic game reduces to the generalized linear complementarity problem (GLCP) with a P-matrix, a well-studied problem whose hardness would imply NP = co-NP. This makes the rich GLCP theory and numerous existing algorithms available for simple stochastic games. As a special case, we get a reduction from binary simple stochastic games to the P-matrix linear complementarity problem (LCP)
Strategy iteration algorithms for games and Markov decision processes
In this thesis, we consider the problem of solving two player infinite games,
such as parity games, mean-payoff games, and discounted games, the problem of
solving Markov decision processes. We study a specific type of algorithm for solving
these problems that we call strategy iteration algorithms. Strategy improvement
algorithms are an example of a type of algorithm that falls under this classification.
We also study Lemke’s algorithm and the Cottle-Dantzig algorithm, which
are classical pivoting algorithms for solving the linear complementarity problem.
The reduction of Jurdzinski and Savani from discounted games to LCPs allows these
algorithms to be applied to infinite games [JS08]. We show that, when they are
applied to games, these algorithms can be viewed as strategy iteration algorithms.
We also resolve the question of their running time on these games by providing a
family of examples upon which these algorithm take exponential time.
Greedy strategy improvement is a natural variation of strategy improvement,
and Friedmann has recently shown an exponential lower bound for this algorithm
when it is applied to infinite games [Fri09]. However, these lower bounds do not
apply for Markov decision processes. We extend Friedmann’s work in order to prove
an exponential lower bound for greedy strategy improvement in the MDP setting.
We also study variations on strategy improvement for infinite games. We
show that there are structures in these games that current strategy improvement
algorithms do not take advantage of. We also show that lower bounds given by
Friedmann [Fri09], and those that are based on his work [FHZ10], work because they
exploit this ignorance. We use our insight to design strategy improvement algorithms
that avoid poor performance caused by the structures that these examples use
Strategy iteration algorithms for games and Markov decision processes
In this thesis, we consider the problem of solving two player infinite games, such as parity games, mean-payoff games, and discounted games, the problem of solving Markov decision processes. We study a specific type of algorithm for solving these problems that we call strategy iteration algorithms. Strategy improvement algorithms are an example of a type of algorithm that falls under this classification. We also study Lemke’s algorithm and the Cottle-Dantzig algorithm, which are classical pivoting algorithms for solving the linear complementarity problem. The reduction of Jurdzinski and Savani from discounted games to LCPs allows these algorithms to be applied to infinite games [JS08]. We show that, when they are applied to games, these algorithms can be viewed as strategy iteration algorithms. We also resolve the question of their running time on these games by providing a family of examples upon which these algorithm take exponential time. Greedy strategy improvement is a natural variation of strategy improvement, and Friedmann has recently shown an exponential lower bound for this algorithm when it is applied to infinite games [Fri09]. However, these lower bounds do not apply for Markov decision processes. We extend Friedmann’s work in order to prove an exponential lower bound for greedy strategy improvement in the MDP setting. We also study variations on strategy improvement for infinite games. We show that there are structures in these games that current strategy improvement algorithms do not take advantage of. We also show that lower bounds given by Friedmann [Fri09], and those that are based on his work [FHZ10], work because they exploit this ignorance. We use our insight to design strategy improvement algorithms that avoid poor performance caused by the structures that these examples use.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Strategy iteration algorithms for games and Markov decision processes
In this thesis, we consider the problem of solving two player infinite games, such as parity games, mean-payoff games, and discounted games, the problem of solving Markov decision processes. We study a specific type of algorithm for solving these problems that we call strategy iteration algorithms. Strategy improvement algorithms are an example of a type of algorithm that falls under this classification. We also study Lemke’s algorithm and the Cottle-Dantzig algorithm, which are classical pivoting algorithms for solving the linear complementarity problem. The reduction of Jurdzinski and Savani from discounted games to LCPs allows these algorithms to be applied to infinite games [JS08]. We show that, when they are applied to games, these algorithms can be viewed as strategy iteration algorithms. We also resolve the question of their running time on these games by providing a family of examples upon which these algorithm take exponential time. Greedy strategy improvement is a natural variation of strategy improvement, and Friedmann has recently shown an exponential lower bound for this algorithm when it is applied to infinite games [Fri09]. However, these lower bounds do not apply for Markov decision processes. We extend Friedmann’s work in order to prove an exponential lower bound for greedy strategy improvement in the MDP setting. We also study variations on strategy improvement for infinite games. We show that there are structures in these games that current strategy improvement algorithms do not take advantage of. We also show that lower bounds given by Friedmann [Fri09], and those that are based on his work [FHZ10], work because they exploit this ignorance. We use our insight to design strategy improvement algorithms that avoid poor performance caused by the structures that these examples use.EThOS - Electronic Theses Online ServiceGBUnited Kingdo