1,963 research outputs found
The Unreasonable Success of Local Search: Geometric Optimization
What is the effectiveness of local search algorithms for geometric problems
in the plane? We prove that local search with neighborhoods of magnitude
is an approximation scheme for the following problems in the
Euclidian plane: TSP with random inputs, Steiner tree with random inputs,
facility location (with worst case inputs), and bicriteria -median (also
with worst case inputs). The randomness assumption is necessary for TSP
Online Correlation Clustering
We study the online clustering problem where data items arrive in an online
fashion. The algorithm maintains a clustering of data items into similarity
classes. Upon arrival of v, the relation between v and previously arrived items
is revealed, so that for each u we are told whether v is similar to u. The
algorithm can create a new cluster for v and merge existing clusters.
When the objective is to minimize disagreements between the clustering and
the input, we prove that a natural greedy algorithm is O(n)-competitive, and
this is optimal.
When the objective is to maximize agreements between the clustering and the
input, we prove that the greedy algorithm is .5-competitive; that no online
algorithm can be better than .834-competitive; we prove that it is possible to
get better than 1/2, by exhibiting a randomized algorithm with competitive
ratio .5+c for a small positive fixed constant c.Comment: 12 pages, 1 figur
First-Come-First-Served for Online Slot Allocation and Huffman Coding
Can one choose a good Huffman code on the fly, without knowing the underlying
distribution? Online Slot Allocation (OSA) models this and similar problems:
There are n slots, each with a known cost. There are n items. Requests for
items are drawn i.i.d. from a fixed but hidden probability distribution p.
After each request, if the item, i, was not previously requested, then the
algorithm (knowing the slot costs and the requests so far, but not p) must
place the item in some vacant slot j(i). The goal is to minimize the sum, over
the items, of the probability of the item times the cost of its assigned slot.
The optimal offline algorithm is trivial: put the most probable item in the
cheapest slot, the second most probable item in the second cheapest slot, etc.
The optimal online algorithm is First Come First Served (FCFS): put the first
requested item in the cheapest slot, the second (distinct) requested item in
the second cheapest slot, etc. The optimal competitive ratios for any online
algorithm are 1+H(n-1) ~ ln n for general costs and 2 for concave costs. For
logarithmic costs, the ratio is, asymptotically, 1: FCFS gives cost opt + O(log
opt).
For Huffman coding, FCFS yields an online algorithm (one that allocates
codewords on demand, without knowing the underlying probability distribution)
that guarantees asymptotically optimal cost: at most opt + 2 log(1+opt) + 2.Comment: ACM-SIAM Symposium on Discrete Algorithms (SODA) 201
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