19,480 research outputs found
Exploring Photometric Redshifts as an Optimization Problem: An Ensemble MCMC and Simulated Annealing-Driven Template-Fitting Approach
Using a grid of million elements () adapted from
COSMOS photometric redshift (photo-z) searches, we investigate the general
properties of template-based photo-z likelihood surfaces. We find these
surfaces are filled with numerous local minima and large degeneracies that
generally confound rapid but "greedy" optimization schemes, even with
additional stochastic sampling methods. In order to robustly and efficiently
explore these surfaces, we develop BAD-Z [Brisk Annealing-Driven Redshifts
(Z)], which combines ensemble Markov Chain Monte Carlo (MCMC) sampling with
simulated annealing to sample arbitrarily large, pre-generated grids in
approximately constant time. Using a mock catalog of 384,662 objects, we show
BAD-Z samples times more efficiently compared to a brute-force
counterpart while maintaining similar levels of accuracy. Our results represent
first steps toward designing template-fitting photo-z approaches limited mainly
by memory constraints rather than computation time.Comment: 14 pages, 8 figures; submitted to MNRAS; comments welcom
Parallel Deterministic and Stochastic Global Minimization of Functions with Very Many Minima
The optimization of three problems with high dimensionality and many local minima are investigated
under five different optimization algorithms: DIRECT, simulated annealing, Spallâs SPSA algorithm, the KNITRO
package, and QNSTOP, a new algorithm developed at Indiana University
Distribution on Warp Maps for Alignment of Open and Closed Curves
Alignment of curve data is an integral part of their statistical analysis,
and can be achieved using model- or optimization-based approaches. The
parameter space is usually the set of monotone, continuous warp maps of a
domain. Infinite-dimensional nature of the parameter space encourages sampling
based approaches, which require a distribution on the set of warp maps.
Moreover, the distribution should also enable sampling in the presence of
important landmark information on the curves which constrain the warp maps. For
alignment of closed and open curves in , possibly with
landmark information, we provide a constructive, point-process based definition
of a distribution on the set of warp maps of and the unit circle
that is (1) simple to sample from, and (2) possesses the
desiderata for decomposition of the alignment problem with landmark constraints
into multiple unconstrained ones. For warp maps on , the distribution is
related to the Dirichlet process. We demonstrate its utility by using it as a
prior distribution on warp maps in a Bayesian model for alignment of two
univariate curves, and as a proposal distribution in a stochastic algorithm
that optimizes a suitable alignment functional for higher-dimensional curves.
Several examples from simulated and real datasets are provided
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