89,235 research outputs found
On the Adaptive Partition Approach to the Detection of Multiple Change-Points
With an adaptive partition procedure, we can partition a “time
course” into consecutive non-overlapped intervals such that the population
means/proportions of the observations in two adjacent intervals are
significantly different at a given level . However, the
widely used recursive combination or partition procedures do not guarantee a
global optimization. We propose a modified dynamic programming algorithm to
achieve a global optimization. Our method can provide consistent estimation
results. In a comprehensive simulation study, our method shows an improved
performance when it is compared to the recursive combination/partition
procedures. In practice, can be determined
based on a cross-validation procedure. As an application, we consider the
well-known Pima Indian Diabetes data. We explore the relationship among the
diabetes risk and several important variables including the plasma glucose
concentration, body mass index and age
An Efficient Global Optimization Algorithm with Adaptive Estimates of the Local Lipschitz Constants
In this work, we present a new deterministic partition-based Global
Optimization (GO) algorithm that uses estimates of the local Lipschitz
constants associated with different sub-regions of the domain of the objective
function. The estimates of the local Lipschitz constants associated with each
partition are the result of adaptively balancing the global and local
information obtained so far from the algorithm, given in terms of absolute
slopes. We motivate a coupling strategy with local optimization algorithms to
accelerate the convergence speed of the proposed approach. In the end, we
compare our approach HALO (Hybrid Adaptive Lipschitzian Optimization) with
respect to popular GO algorithms using hundreds of test functions. From the
numerical results, the performance of HALO is very promising and can extend our
arsenal of efficient procedures for attacking challenging real-world GO
problems. The Python code of HALO is publicly available on GitHub.
https://github.com/dannyzx/HAL
Multiresolution co-clustering for uncalibrated multiview segmentation
We propose a technique for coherently co-clustering uncalibrated views of a scene with a contour-based representation. Our work extends the previous framework, an iterative algorithm for segmenting sequences with small variations, where the partition solution space is too restrictive for scenarios where consecutive images present larger variations. To deal with a more flexible scenario, we present three main contributions. First, motion information has been considered both for region adjacency and region similarity. Second, a two-step iterative architecture is proposed to increase the partition solution space. Third, a feasible global optimization that allows to jointly process all the views has been implemented. In addition to the previous contributions, which are based on low-level features, we have also considered introducing higher level features as semantic information in the co-clustering algorithm. We evaluate these techniques on multiview and temporal datasets, showing that they outperform state-of-the-art approaches.Peer ReviewedPostprint (author's final draft
Lipschitz gradients for global optimization in a one-point-based partitioning scheme
A global optimization problem is studied where the objective function
is a multidimensional black-box function and its gradient satisfies the
Lipschitz condition over a hyperinterval with an unknown Lipschitz constant
. Different methods for solving this problem by using an a priori given
estimate of , its adaptive estimates, and adaptive estimates of local
Lipschitz constants are known in the literature. Recently, the authors have
proposed a one-dimensional algorithm working with multiple estimates of the
Lipschitz constant for (the existence of such an algorithm was a
challenge for 15 years). In this paper, a new multidimensional geometric method
evolving the ideas of this one-dimensional scheme and using an efficient
one-point-based partitioning strategy is proposed. Numerical experiments
executed on 800 multidimensional test functions demonstrate quite a promising
performance in comparison with popular DIRECT-based methods.Comment: 25 pages, 4 figures, 5 tables. arXiv admin note: text overlap with
arXiv:1103.205
A multi-objective DIRECT algorithm for ship hull optimization
The paper is concerned with black-box nonlinear constrained multi-objective optimization problems. Our interest is the definition of a multi-objective deterministic partition-based algorithm. The main target of the proposed algorithm is the solution of a real ship hull optimization problem. To this purpose and in pursuit of an efficient method, we develop an hybrid algorithm by coupling a multi-objective DIRECT-type algorithm with an efficient derivative-free local algorithm. The results obtained on a set of “hard” nonlinear constrained multi-objective test problems show viability of the proposed approach. Results on a hull-form optimization of a high-speed catamaran (sailing in head waves in the North Pacific Ocean) are also presented. In order to consider a real ocean environment, stochastic sea state and speed are taken into account. The problem is formulated as a multi-objective optimization aimed at (i) the reduction of the expected value of the mean total resistance in irregular head waves, at variable speed and (ii) the increase of the ship operability, with respect to a set of motion-related constraints. We show that the hybrid method performs well also on this industrial problem
- …