Phase transition and landscape statistics of the number partitioning problem

The phase transition in the number partitioning problem (NPP), i.e., the transition from a region in the space of control parameters in which almost all instances have many solutions to a region in which almost all instances have no solution, is investigated by examining the energy landscape of this classic optimization problem. This is achieved by coding the information about the minimum energy paths connecting pairs of minima into a tree structure, termed a barrier tree, the leaves and internal nodes of which represent, respectively, the minima and the lowest energy saddles connecting those minima. Here we apply several measures of shape (balance and symmetry) as well as of branch lengths (barrier heights) to the barrier trees that result from the landscape of the NPP, aiming at identifying traces of the easy/hard transition. We find that it is not possible to tell the easy regime from the hard one by visual inspection of the trees or by measuring the barrier heights. Only the {\it difficulty} measure, given by the maximum value of the ratio between the barrier height and the energy surplus of local minima, succeeded in detecting traces of the phase transition in the tree. In adddition, we show that the barrier trees associated with the NPP are very similar to random trees, contrasting dramatically with trees associated with the $p$ spin-glass and random energy models. We also examine critically a recent conjecture on the equivalence between the NPP and a truncated random energy model

A Unified approach to concurrent and parallel algorithms on balanced data structures

Concurrent and parallel algorithms are different. However, in the case of dictionaries, both kinds of algorithms share many common points. We present a unified approach emphasizing these points. It is based on a careful analysis of the sequential algorithm, extracting from it the more basic facts, encapsulated later on as local rules. We apply the method to the insertion algorithms in AVL trees. All the concurrent and parallel insertion algorithms have two main phases. A percolation phase, moving the keys to be inserted down, and a rebalancing phase. Finally, some other algorithms and balanced structures are discussed.Postprint (published version

Dynamic Ordered Sets with Exponential Search Trees

We introduce exponential search trees as a novel technique for converting static polynomial space search structures for ordered sets into fully-dynamic linear space data structures. This leads to an optimal bound of O(sqrt(log n/loglog n)) for searching and updating a dynamic set of n integer keys in linear space. Here searching an integer y means finding the maximum key in the set which is smaller than or equal to y. This problem is equivalent to the standard text book problem of maintaining an ordered set (see, e.g., Cormen, Leiserson, Rivest, and Stein: Introduction to Algorithms, 2nd ed., MIT Press, 2001). The best previous deterministic linear space bound was O(log n/loglog n) due Fredman and Willard from STOC 1990. No better deterministic search bound was known using polynomial space. We also get the following worst-case linear space trade-offs between the number n, the word length w, and the maximal key U < 2^w: O(min{loglog n+log n/log w, (loglog n)(loglog U)/(logloglog U)}). These trade-offs are, however, not likely to be optimal. Our results are generalized to finger searching and string searching, providing optimal results for both in terms of n.Comment: Revision corrects some typoes and state things better for applications in subsequent paper

Foraging under Predation Risk: A test of giving-up densities with samango monkeys in South Africa

Animals frequently make a trade-off between food and safety and will sacrifice feeding effort if it means safety from predators. A forager can also vary its vigilance levels to manage predation risk. Giving-up densities (GUDs), the amount of food items left once a forager has quit an experimental food patch, have been used extensively as measures of foraging behaviour under risk of predation in a wide range of species. Vigilance also serves as an anti-predatory response to predation risk and has been the focus of a range of behavioural studies. However, very few studies have looked at these two measures together. The principal aim of this study was to determine the effect of habitat factors on the foraging behaviour of samango monkeys (Cercopithcus mitis erythrarchus) by measuring GUDs in artificial food patches and foraging behaviour, and relating this to height from the ground, canopy cover, habitat visibility and observed behaviour. The second objective was then to determine the extent to which the experimental approach matched observed behaviour in measuring primate responses to predation risk. The monkeys revealed lower GUDs with increasing height and with decreasing canopy cover and but were not affected by habitat visibility. Vigilance varied considerably with only conspecific and observer vigilance showing significant effects. Conspecific vigilance increased with height and decreasing canopy cover. Vigilance directed at observers increased with decreasing canopy cover. There was no effect of habitat visibility on any of the component behaviours of vigilance. The vigilance behaviour of the monkeys did not completely compliment the GUD results. The findings of this study confirm the prediction that habitat plays a key role in the foraging behaviour of samango monkeys but that vigilance is more sensitive to other factors such as sociality. Further work is required to determine the extent to which experimental approaches based on giving up densities match patterns of antipredatory behaviour recorded by observational methods

Analysis of approximate nearest neighbor searching with clustered point sets

We present an empirical analysis of data structures for approximate nearest neighbor searching. We compare the well-known optimized kd-tree splitting method against two alternative splitting methods. The first, called the sliding-midpoint method, which attempts to balance the goals of producing subdivision cells of bounded aspect ratio, while not producing any empty cells. The second, called the minimum-ambiguity method is a query-based approach. In addition to the data points, it is also given a training set of query points for preprocessing. It employs a simple greedy algorithm to select the splitting plane that minimizes the average amount of ambiguity in the choice of the nearest neighbor for the training points. We provide an empirical analysis comparing these two methods against the optimized kd-tree construction for a number of synthetically generated data and query sets. We demonstrate that for clustered data and query sets, these algorithms can provide significant improvements over the standard kd-tree construction for approximate nearest neighbor searching.Comment: 20 pages, 8 figures. Presented at ALENEX '99, Baltimore, MD, Jan 15-16, 199