1,623 research outputs found
On the Computational Power of DNA Annealing and Ligation
In [20] it was shown that the DNA primitives of Separate,
Merge, and Amplify were not sufficiently powerful to invert
functions defined by circuits in linear time. Dan Boneh et
al [4] show that the addition of a ligation primitive, Append, provides the missing power. The question becomes, "How powerful is ligation? Are Separate, Merge, and Amplify
necessary at all?" This paper proposes to informally explore
the power of annealing and ligation for DNA computation.
We conclude, in fact, that annealing and ligation alone are
theoretically capable of universal computation
Variable Annealing Length and Parallelism in Simulated Annealing
In this paper, we propose: (a) a restart schedule for an adaptive simulated
annealer, and (b) parallel simulated annealing, with an adaptive and
parameter-free annealing schedule. The foundation of our approach is the
Modified Lam annealing schedule, which adaptively controls the temperature
parameter to track a theoretically ideal rate of acceptance of neighboring
states. A sequential implementation of Modified Lam simulated annealing is
almost parameter-free. However, it requires prior knowledge of the annealing
length. We eliminate this parameter using restarts, with an exponentially
increasing schedule of annealing lengths. We then extend this restart schedule
to parallel implementation, executing several Modified Lam simulated annealers
in parallel, with varying initial annealing lengths, and our proposed parallel
annealing length schedule. To validate our approach, we conduct experiments on
an NP-Hard scheduling problem with sequence-dependent setup constraints. We
compare our approach to fixed length restarts, both sequentially and in
parallel. Our results show that our approach can achieve substantial
performance gains, throughout the course of the run, demonstrating our approach
to be an effective anytime algorithm.Comment: Tenth International Symposium on Combinatorial Search, pages 2-10.
June 201
Accelerating MCMC via Parallel Predictive Prefetching
We present a general framework for accelerating a large class of widely used
Markov chain Monte Carlo (MCMC) algorithms. Our approach exploits fast,
iterative approximations to the target density to speculatively evaluate many
potential future steps of the chain in parallel. The approach can accelerate
computation of the target distribution of a Bayesian inference problem, without
compromising exactness, by exploiting subsets of data. It takes advantage of
whatever parallel resources are available, but produces results exactly
equivalent to standard serial execution. In the initial burn-in phase of chain
evaluation, it achieves speedup over serial evaluation that is close to linear
in the number of available cores
Satisfiability Test with Synchronous Simulated Annealing on the Fujitsu AP1000 Massively-Parallel Multiprocessor
Solving the hard Satisfiability Problem is time consuming even for modest-sized problem instances. Solving the Random L-SAT Problem is especially difficult due to the ratio of clauses to variables. This report presents a parallel synchronous simulated annealing method for solving the Random L-SAT Problem on a large-scale distributed-memory multiprocessor. In particular, we use a parallel synchronous simulated annealing procedure, called Generalized Speculative Computation, which guarantees the same decision sequence as sequential simulated annealing. To demonstrate the performance of the parallel method, we have selected problem instances varying in size from 100-variables/425-clauses to 5000-variables/21,250-clauses. Experimental results on the AP1000 multiprocessor indicate that our approach can satisfy 99.9 percent of the clauses while giving almost a 70-fold speedup on 500 processors
Stochastic Speculative Computation Method and its Application to Monte Carlo Molecular Simulation
Monte Carlo (MC) molecular simulation has significant computational complexity, and parallel processing is considered effective for computation of problems with large complexity. In recent years, multicore or many-core processors have gained significant attention as they enable computation with a large degree of parallelism on desktop computers. However, in conventional parallel processing, processes must be synchronized frequently; thus, parallel computing is not necessarily efficient. In this study, we evaluate the effect of applying MultiStart-based speculative parallel computation to MC simulations. Using probability theory, we performed theoretical verification to determine if speculative computation is more effective than conventional parallel computation methods. The parameters obtained from the theoretical calculations were observed in experiments wherein the speculative method was applied to an MC molecular simulation. In this paper, we report the results of the theoretical verification and experiments, and we show that speculative computation can accelerate MC molecular simulations
Enhanced global optimization methods applied to complex fisheries stock assessment models
[Abstract]
Statistical fisheries models are frequently used by researchers and agencies to understand the behavior of marine ecosystems or to estimate the maximum acceptable catch of different species of commercial interest. The parameters of these models are usually adjusted through the use of optimization algorithms. Unfortunately, the choice of the best optimization method is far from trivial. This work proposes the use of population-based algorithms to improve the optimization process of the Globally applicable Area Disaggregated General Ecosystem Toolbox (Gadget), a flexible framework that allows the development of complex statistical marine ecosystem models. Specifically, parallel versions of the Differential Evolution (DE) and the Particle Swarm Optimization (PSO) methods are proposed. The proposals include an automatic selection of the internal parameters to reduce the complexity of their usage, and a restart mechanism to avoid local minima. The resulting optimization algorithms were called PMA (Parallel Multirestart Adaptive) DE and PMA PSO respectively. Experimental results prove that the new algorithms are faster and produce more accurate solutions than the other parallel optimization methods already included in Gadget. Although the new proposals have been evaluated on fisheries models, there is nothing specific to the tested models in them, and thus they can be also applied to other optimization problems. Moreover, the PMA scheme proposed can be seen as a template that can be easily applied to other population-based heuristics.Xunta de Galicia; ED431C 2017/04Xunta de Galicia; R2016/0
NP-complete Problems and Physical Reality
Can NP-complete problems be solved efficiently in the physical universe? I
survey proposals including soap bubbles, protein folding, quantum computing,
quantum advice, quantum adiabatic algorithms, quantum-mechanical
nonlinearities, hidden variables, relativistic time dilation, analog computing,
Malament-Hogarth spacetimes, quantum gravity, closed timelike curves, and
"anthropic computing." The section on soap bubbles even includes some
"experimental" results. While I do not believe that any of the proposals will
let us solve NP-complete problems efficiently, I argue that by studying them,
we can learn something not only about computation but also about physics.Comment: 23 pages, minor correction
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