19,334 research outputs found
Pure adaptive search in monte carlo optimization
Pure adaptive search constructs a sequence of points uniformly distributed within a corresponding sequence of nested regions of the feasible space. At any stage, the next point in the sequence is chosen uniformly distributed over the region of feasible space containing all points that are equal or superior in value to the previous points in the sequence. We show that for convex programs the number of iterations required to achieve a given accuracy of solution increases at most linearly in the dimension of the problem. This compares to exponential growth in iterations required for pure random search.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47920/1/10107_2005_Article_BF01582296.pd
Particle algorithms for optimization on binary spaces
We discuss a unified approach to stochastic optimization of pseudo-Boolean
objective functions based on particle methods, including the cross-entropy
method and simulated annealing as special cases. We point out the need for
auxiliary sampling distributions, that is parametric families on binary spaces,
which are able to reproduce complex dependency structures, and illustrate their
usefulness in our numerical experiments. We provide numerical evidence that
particle-driven optimization algorithms based on parametric families yield
superior results on strongly multi-modal optimization problems while local
search heuristics outperform them on easier problems
Fully coherent follow-up of continuous gravitational-wave candidates
The search for continuous gravitational waves from unknown isolated sources
is computationally limited due to the enormous parameter space that needs to be
covered and the weakness of the expected signals. Therefore semi-coherent
search strategies have been developed and applied in distributed computing
environments such as Einstein@Home, in order to narrow down the parameter space
and identify interesting candidates. However, in order to optimally confirm or
dismiss a candidate as a possible gravitational-wave signal, a fully-coherent
follow-up using all the available data is required.
We present a general method and implementation of a direct (2-stage)
transition to a fully-coherent follow-up on semi-coherent candidates. This
method is based on a grid-less Mesh Adaptive Direct Search (MADS) algorithm
using the F-statistic. We demonstrate the detection power and computing cost of
this follow-up procedure using extensive Monte-Carlo simulations on (simulated)
semi-coherent candidates from a directed as well as from an all-sky search
setup.Comment: 12 pages, 5 figure
Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling
Greedy heuristics may be attuned by looking ahead for each possible choice,
in an approach called the rollout or Pilot method. These methods may be seen as
meta-heuristics that can enhance (any) heuristic solution, by repetitively
modifying a master solution: similarly to what is done in game tree search,
better choices are identified using lookahead, based on solutions obtained by
repeatedly using a greedy heuristic. This paper first illustrates how the Pilot
method improves upon some simple well known dispatch heuristics for the
job-shop scheduling problem. The Pilot method is then shown to be a special
case of the more recent Monte Carlo Tree Search (MCTS) methods: Unlike the
Pilot method, MCTS methods use random completion of partial solutions to
identify promising branches of the tree. The Pilot method and a simple version
of MCTS, using the -greedy exploration paradigms, are then
compared within the same framework, consisting of 300 scheduling problems of
varying sizes with fixed-budget of rollouts. Results demonstrate that MCTS
reaches better or same results as the Pilot methods in this context.Comment: Learning and Intelligent OptimizatioN (LION'6) 7219 (2012
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