25,594 research outputs found
Generalized Weiszfeld algorithms for Lq optimization
In many computer vision applications, a desired model of some type is computed by minimizing a cost function based on several measurements. Typically, one may compute the model that minimizes the L₂ cost, that is the sum of squares of measurement errors with respect to the model. However, the Lq solution which minimizes the sum of the qth power of errors usually gives more robust results in the presence of outliers for some values of q, for example, q = 1. The Weiszfeld algorithm is a classic algorithm for finding the geometric L1 mean of a set of points in Euclidean space. It is provably optimal and requires neither differentiation, nor line search. The Weiszfeld algorithm has also been generalized to find the L1 mean of a set of points on a Riemannian manifold of non-negative curvature. This paper shows that the Weiszfeld approach may be extended to a wide variety of problems to find an Lq mean for 1 ≤ q <; 2, while maintaining simplicity and provable convergence. We apply this problem to both single-rotation averaging (under which the algorithm provably finds the global Lq optimum) and multiple rotation averaging (for which no such proof exists). Experimental results of Lq optimization for rotations show the improved reliability and robustness compared to L₂ optimization.This research has been funded by National ICT Australia
A matrix-algebraic algorithm for the Riemannian logarithm on the Stiefel manifold under the canonical metric
We derive a numerical algorithm for evaluating the Riemannian logarithm on
the Stiefel manifold with respect to the canonical metric. In contrast to the
existing optimization-based approach, we work from a purely matrix-algebraic
perspective. Moreover, we prove that the algorithm converges locally and
exhibits a linear rate of convergence.Comment: 30 pages, 5 figures, Matlab cod
A Self-learning Algebraic Multigrid Method for Extremal Singular Triplets and Eigenpairs
A self-learning algebraic multigrid method for dominant and minimal singular
triplets and eigenpairs is described. The method consists of two multilevel
phases. In the first, multiplicative phase (setup phase), tentative singular
triplets are calculated along with a multigrid hierarchy of interpolation
operators that approximately fit the tentative singular vectors in a collective
and self-learning manner, using multiplicative update formulas. In the second,
additive phase (solve phase), the tentative singular triplets are improved up
to the desired accuracy by using an additive correction scheme with fixed
interpolation operators, combined with a Ritz update. A suitable generalization
of the singular value decomposition is formulated that applies to the coarse
levels of the multilevel cycles. The proposed algorithm combines and extends
two existing multigrid approaches for symmetric positive definite eigenvalue
problems to the case of dominant and minimal singular triplets. Numerical tests
on model problems from different areas show that the algorithm converges to
high accuracy in a modest number of iterations, and is flexible enough to deal
with a variety of problems due to its self-learning properties.Comment: 29 page
Logarithmic link smearing for full QCD
A Lie-algebra based recipe for smoothing gauge links in lattice field theory
is presented, building on the matrix logarithm. With or without hypercubic
nesting, this LOG/HYL smearing yields fat links which are differentiable w.r.t.
the original ones. This is essential for defining UV-filtered ("fat link")
fermion actions which may be simulated with a HMC-type algorithm. The effect of
this smearing on the distribution of plaquettes and on the residual mass of
tree-level O(a)-improved clover fermions in quenched QCD is studied.Comment: 29 pages, 7 figures; v2: improved text, includes comparison of
APE/EXP/LOG with optimized parameters, 3 references adde
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