Tree-Independent Dual-Tree Algorithms

Dual-tree algorithms are a widely used class of branch-and-bound algorithms. Unfortunately, developing dual-tree algorithms for use with different trees and problems is often complex and burdensome. We introduce a four-part logical split: the tree, the traversal, the point-to-point base case, and the pruning rule. We provide a meta-algorithm which allows development of dual-tree algorithms in a tree-independent manner and easy extension to entirely new types of trees. Representations are provided for five common algorithms; for k-nearest neighbor search, this leads to a novel, tighter pruning bound. The meta-algorithm also allows straightforward extensions to massively parallel settings.Comment: accepted in ICML 201

High field properties of geometrically frustrated magnets

Above the saturation field, geometrically frustrated quantum antiferromagnets have dispersionless low-energy branches of excitations corresponding to localized spin-flip modes. Transition into a partially magnetized state occurs via condensation of an infinite number of degrees of freedom. The ground state below the phase transition is a magnon crystal, which breaks only translational symmetry and preserves spin-rotations about the field direction. We give a detailed review of recent works on physics of such phase transitions and present further theoretical developments. Specifically, the low-energy degrees of freedom of a spin-1/2 kagom\'e antiferromagnet are mapped to a hard hexagon gas on a triangular lattice. Such a mapping allows to obtain a quantitative description of the magnetothermodynamics of a quantum kagom\'e antiferromagnet from the exact solution for a hard hexagon gas. In particular, we find the exact critical behavior at the transition into a magnon crystal state, the universal value of the entropy at the saturation field, and the position of peaks in temperature- and field-dependence of the specific heat. Analogous mapping is presented for the sawtooth chain, which is mapped onto a model of classical hard dimers on a chain. The finite macroscopic entropies of geometrically frustrated magnets at the saturation field lead to a large magnetocaloric effect.Comment: 22 pages, proceedings of YKIS2004 worksho

Spin excitations used to probe the nature of the exchange coupling in the magnetically ordered ground state of Pr0.5_{0.5}Ca0.5_{0.5}MnO3_{3}

We have used time-of-flight inelastic neutron scattering to measure the spin wave spectrum of the canonical half-doped manganite Pr$_{0.5}$Ca$_{0.5}$MnO$_{3}$, in its magnetic and orbitally ordered phase. The data, which cover multiple Brillouin zones and the entire energy range of the excitations, are compared with several different models that are all consistent with the CE-type magnetic order, but arise through different exchange coupling schemes. The Goodenough model, i.e. an ordered state comprising strong nearest neighbor ferromagnetic interactions along zig-zag chains with antiferromagnetic inter-chain coupling, provides the best description of the data, provided that further neighbor interactions along the chains are included. We are able to rule out a coupling scheme involving formation of strongly bound ferromagnetic dimers, i.e. Zener polarons, on the basis of gross features of the observed spin wave spectrum. A model with weaker dimerization reproduces the observed dispersion but can be ruled out on the basis of discrepancies between the calculated and observed structure factors at certain positions in reciprocal space. Adding further neighbor interactions results in almost no dimerization, i.e. recovery of the Goodenough model. These results are consistent with theoretical analysis of the degenerate double exchange model for half-doping, and provide a recipe for how to interpret future measurements away from half-doping, where degenerate double exchange models predict more complex ground states.Comment: 14 pages, 11 figure

Sampling-based Algorithms for Optimal Motion Planning

During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i.e., such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.Comment: 76 pages, 26 figures, to appear in International Journal of Robotics Researc