Randomized Approximation Algorithms

Efficient Approximation Algorithms for Point-set Diameter in Higher Dimensions

We study the problem of computing the diameter of a  set of $n$ points in $d$-dimensional Euclidean space for a fixed dimension $d$, and propose a new $(1+varepsilon)$-approximation algorithm with $O(n+ 1/varepsilon^{d-1})$ time and $O(n)$ space, where $0 < varepsilonleqslant 1$. We also show that the proposed algorithm can be modified to a $(1+O(varepsilon))$-approximation algorithm with $O(n+...

Design Algorithms for Doppler MTI and Filters (Using Chebyshev Approximation)

Sample-and-Accumulate Algorithms for Belief Updating in Bayes Networks

Belief updating in Bayes nets, a well known computationally hard problem, has recently been approximated by several deterministic algorithms, and by various randomized approximation algorithlns. Deterministic algorithms usually provide probability bounds, but have an exponential runtime. Some randomized schemes haw~, a polynomial runtime, but provide only probability estimates. We present rando...

08201 Abstracts Collection - Design and Analysis of Randomized and Approximation Algorithms

The Dagstuhl Seminar on “Design and Analysis of Randomized and Approximation Algorithms” (Seminar 11241) was held at Schloss Dagstuhl between June 13–17, 2011. There were 26 regular talks and several informal and open problem session contributions presented during this seminar. Abstracts of the presentations have been put together in this seminar proceedings document together with some links to...

A Deterministic Analysis of Stochastic Approximation with Randomized Directions - Automatic Control, IEEE Transactions on

We study the convergence of two stochastic approximation algorithms with randomized directions: the simultaneous perturbation stochastic approximation algorithm and the random direction Kiefer–Wolfowitz algorithm. We establish deterministic necessary and sufficient conditions on the random directions and noise sequences for both algorithms, and these conditions demonstrate the effect of the “ra...

A Deterministic Analysis of Stochastic Approximation with Randomized Directions

We study the convergence of two stochastic approximation algorithms with randomized directions: the simultaneous perturbation stochastic approximation algorithm and the random direction Kiefer–Wolfowitz algorithm. We establish deterministic necessary and sufficient conditions on the random directions and noise sequences for both algorithms, and these conditions demonstrate the effect of the “ra...