56,175 research outputs found
Distributed Approximation of Fixed-Points in Trust Structures
Recently, Carbone, Nielsen and Sassone introduced the trust-structure framework; a semantic model for trust-management in global-scale distributed systems. The framework is based on the notion of trust structures; a set of ``trust-levels'' ordered by two distinct partial orderings. In the model, a unique global trust-state is defined as the least fixed-point of a collection of local policies assigning trust-levels to the entities of the system. However, the framework is a purely denotational model: it gives precise meaning to the global trust-state of a system, but without specifying a way to compute this abstract mathematical object. This paper complements q the denotational model of trust structures with operational techniques. It is shown how the least fixed-point can be computed using a simple, totally-asynchronous distributed algorithm. Two efficient protocols for approximating the least fixed-point are provided, enabling sound reasoning about the global trust-state without computing the exact fixed-point. Finally, dynamic algorithms are presented for safe reuse of information between computations, in face of dynamic trust-policy updates
Evaluation of advanced optimisation methods for estimating Mixed Logit models
The performances of different simulation-based estimation techniques for mixed logit modeling are evaluated. A quasi-Monte Carlo method (modified Latin hypercube sampling) is compared with a Monte Carlo algorithm with dynamic accuracy. The classic Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm line-search approach and trust region methods, which have proved to be extremely powerful in nonlinear programming, are also compared. Numerical tests are performed on two real data sets: stated preference data for parking type collected in the United Kingdom, and revealed preference data for mode choice collected as part of a German travel diary survey. Several criteria are used to evaluate the approximation quality of the log likelihood function and the accuracy of the results and the associated estimation runtime. Results suggest that the trust region approach outperforms the BFGS approach and that Monte Carlo methods remain competitive with quasi-Monte Carlo methods in high-dimensional problems, especially when an adaptive optimization algorithm is used
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
A central challenge to many fields of science and engineering involves
minimizing non-convex error functions over continuous, high dimensional spaces.
Gradient descent or quasi-Newton methods are almost ubiquitously used to
perform such minimizations, and it is often thought that a main source of
difficulty for these local methods to find the global minimum is the
proliferation of local minima with much higher error than the global minimum.
Here we argue, based on results from statistical physics, random matrix theory,
neural network theory, and empirical evidence, that a deeper and more profound
difficulty originates from the proliferation of saddle points, not local
minima, especially in high dimensional problems of practical interest. Such
saddle points are surrounded by high error plateaus that can dramatically slow
down learning, and give the illusory impression of the existence of a local
minimum. Motivated by these arguments, we propose a new approach to
second-order optimization, the saddle-free Newton method, that can rapidly
escape high dimensional saddle points, unlike gradient descent and quasi-Newton
methods. We apply this algorithm to deep or recurrent neural network training,
and provide numerical evidence for its superior optimization performance.Comment: The theoretical review and analysis in this article draw heavily from
arXiv:1405.4604 [cs.LG
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
Network Cosmology
Prediction and control of the dynamics of complex networks is a central
problem in network science. Structural and dynamical similarities of different
real networks suggest that some universal laws might accurately describe the
dynamics of these networks, albeit the nature and common origin of such laws
remain elusive. Here we show that the causal network representing the
large-scale structure of spacetime in our accelerating universe is a power-law
graph with strong clustering, similar to many complex networks such as the
Internet, social, or biological networks. We prove that this structural
similarity is a consequence of the asymptotic equivalence between the
large-scale growth dynamics of complex networks and causal networks. This
equivalence suggests that unexpectedly similar laws govern the dynamics of
complex networks and spacetime in the universe, with implications to network
science and cosmology
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