Book Chapter10.1007/3-540-48447-7_17
On an Optimal Split Tree Problem
S. Rao Kosaraju,Teresa M. Przytycka,Ryan S. Borgstrom +2 more
- 11 Aug 1999
- pp 157-168
102
TL;DR: It is shown that if all weights are equal and the optimal split tree is of depth O(log n), then the greedy algorithm guarantees O( log n/log log n) approximation ratio.
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Abstract: We introduce and study a problem that we refer to as the optimal split tree problem. The problem generalizes a number of problems including two classical tree construction problems including the Huffman tree problem and the optimal alphabetic tree. We show that the general split tree problem is NP-complete and analyze a greedy algorithm for its solution. We show that a simple modification of the greedy algorithm guarantees O(log n) approximation ratio. We construct an example for which this algorithm achieves Ω(log n/log log n) approximation ratio. We show that if all weights are equal and the optimal split tree is of depth O(log n). then the greedy algorithm guarantees O(log n/log log n) approximation ratio. We also extend our approximation algorithm to the construction of a search tree for partially ordered sets.
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Citations
Submodular Function Maximization
Andreas Krause,Daniel Golovin +1 more
- 01 Feb 2014
TL;DR: This survey will introduce submodularity and some of its generalizations, illustrate how it arises in various applications, and discuss algorithms for optimizing submodular functions.
Adaptive submodularity: theory and applications in active learning and stochastic optimization
Daniel Golovin,Andreas Krause +1 more
TL;DR: In this article, the concept of adaptive submodularity is introduced, which generalizes submodular set functions to adaptive policies and provides performance guarantees for both stochastic maximization and coverage, and can be exploited to speed up the greedy algorithm by using lazy evaluations.
•Posted Content
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization
Daniel Golovin,Andreas Krause +1 more
TL;DR: It is proved that if a problem satisfies adaptive submodularity, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy, providing performance guarantees for both stochastic maximization and coverage.
•Proceedings Article
Near-Optimal Bayesian Active Learning with Noisy Observations
Daniel Golovin,Andreas Krause,Debajyoti Ray +2 more
- 06 Dec 2010
TL;DR: In this article, a greedy active learning algorithm called EC2 was proposed for Bayesian active learning with noisy observations, and it was shown that it is competitive with the optimal policy.
The Geometry of Generalized Binary Search
TL;DR: In this paper, the authors investigated the problem of determining a binary-valued function through a sequence of strategically selected queries, and developed novel incoherence and geometric conditions under which GBS achieves the information-theoretically optimal query complexity, i.e., given a collection of N hypotheses, GBS terminates with the correct function after no more than a constant times log N queries.
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A method for the construction of minimum-redundancy codes
TL;DR: A minimum-redundancy code is one constructed in such a way that the average number of coding digits per message is minimized.
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