327,771 research outputs found
Active Bayesian Optimization: Minimizing Minimizer Entropy
The ultimate goal of optimization is to find the minimizer of a target
function.However, typical criteria for active optimization often ignore the
uncertainty about the minimizer. We propose a novel criterion for global
optimization and an associated sequential active learning strategy using
Gaussian processes.Our criterion is the reduction of uncertainty in the
posterior distribution of the function minimizer. It can also flexibly
incorporate multiple global minimizers. We implement a tractable approximation
of the criterion and demonstrate that it obtains the global minimizer
accurately compared to conventional Bayesian optimization criteria
Efficiency of attack strategies on complex model and real-world networks
We investigated the efficiency of attack strategies to network nodes when
targeting several complex model and real-world networks. We tested 5 attack
strategies, 3 of which were introduced in this work for the first time, to
attack 3 model (Erdos and Renyi, Barabasi and Albert preferential attachment
network, and scale-free network configuration models) and 3 real networks
(Gnutella peer-to-peer network, email network of the University of Rovira i
Virgili, and immunoglobulin interaction network). Nodes were removed
sequentially according to the importance criterion defined by the attack
strategy. We used the size of the largest connected component (LCC) as a
measure of network damage. We found that the efficiency of attack strategies
(fraction of nodes to be deleted for a given reduction of LCC size) depends on
the topology of the network, although attacks based on the number of
connections of a node and betweenness centrality were often the most efficient
strategies. Sequential deletion of nodes in decreasing order of betweenness
centrality was the most efficient attack strategy when targeting real-world
networks. In particular for networks with power-law degree distribution, we
observed that most efficient strategy change during the sequential removal of
nodes.Comment: 18 pages, 4 figure
Normalisation for Dynamic Pattern Calculi
The Pure Pattern Calculus (PPC) extends the lambda-calculus, as well as the family of algebraic pattern calculi, with first-class patterns; that is, patterns can be passed as arguments, evaluated and returned as results. The notion of matching failure of the PPC not only provides a mechanism to define functions by pattern matching on cases but also supplies PPC with parallel-or-like, non-sequential behaviour. Therefore, devising normalising strategies for PPC to obtain well-behaved implementations turns out to be challenging.
This paper focuses on normalising reduction strategies for PPC. We define a (multistep) strategy and show that it is normalising. The strategy generalises the leftmost-outermost strategy for lambda-calculus and is strictly finer than parallel-outermost. The normalisation proof is based on the notion of necessary set of redexes, a generalisation of the notion of needed redex encompassing
non-sequential reduction systems
Electromagnetic device design based on RBF models and two new sequential optimization strategies
We present two new strategies for sequential optimization method (SOM) to deal with the optimization design problems of electromagnetic devices. One is a new space reduction strategy; the other is model selection strategy. Meanwhile, radial basis function (RBF) and compactly supported RBF models are investigated to extend the applied model types for SOM. Thereafter, Monte Carlo method is employed to demonstrate the efficiency and superiority of the new space reduction strategy. Five commonly used approximate models are considered for the discussion of model selection strategy. Furthermore, by two TEAM benchmark examples, we can see that SOM with the proposed new strategies and models can significantly speed the optimization design process, and the efficiency of SOM depends a little on the types of approximate models. © 2006 IEEE
Smoothed Efficient Algorithms and Reductions for Network Coordination Games
Worst-case hardness results for most equilibrium computation problems have
raised the need for beyond-worst-case analysis. To this end, we study the
smoothed complexity of finding pure Nash equilibria in Network Coordination
Games, a PLS-complete problem in the worst case. This is a potential game where
the sequential-better-response algorithm is known to converge to a pure NE,
albeit in exponential time. First, we prove polynomial (resp. quasi-polynomial)
smoothed complexity when the underlying game graph is a complete (resp.
arbitrary) graph, and every player has constantly many strategies. We note that
the complete graph case is reminiscent of perturbing all parameters, a common
assumption in most known smoothed analysis results.
Second, we define a notion of smoothness-preserving reduction among search
problems, and obtain reductions from -strategy network coordination games to
local-max-cut, and from -strategy games (with arbitrary ) to
local-max-cut up to two flips. The former together with the recent result of
[BCC18] gives an alternate -time smoothed algorithm for the
-strategy case. This notion of reduction allows for the extension of
smoothed efficient algorithms from one problem to another.
For the first set of results, we develop techniques to bound the probability
that an (adversarial) better-response sequence makes slow improvements on the
potential. Our approach combines and generalizes the local-max-cut approaches
of [ER14,ABPW17] to handle the multi-strategy case: it requires a careful
definition of the matrix which captures the increase in potential, a tighter
union bound on adversarial sequences, and balancing it with good enough rank
bounds. We believe that the approach and notions developed herein could be of
interest in addressing the smoothed complexity of other potential and/or
congestion games
Decision making under time pressure: an independent test of sequential sampling models
Choice probability and choice response time data from a risk-taking decision-making task were compared with predictions made by a sequential sampling model. The behavioral data, consistent with the model, showed that participants were less likely to take an action as risk levels increased, and that time pressure did not have a uniform effect on choice probability. Under time pressure, participants were more conservative at the lower risk levels but were more prone to take risks at the higher levels of risk. This crossover interaction reflected a reduction of the threshold within a single decision strategy rather than a switching of decision strategies. Response time data, as predicted by the model, showed that participants took more time to make decisions at the moderate risk levels and that time pressure reduced response time across all risk levels, but particularly at the those risk levels that took longer time with no pressure. Finally, response time data were used to rule out the hypothesis that time pressure effects could be explained by a fast-guess strategy
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