96,718 research outputs found
Quasi-optimal elimination trees for 2D grids with singularities
We construct quasi-optimal elimination trees for 2D finite element meshes with singularities.These trees minimize the complexity of the solution of the discrete system. The computational cost estimates of the elimination process model the execution of the multifrontal algorithms in serial and in parallel shared-memory executions. Since the meshes considered are a subspace of all possible mesh partitions, we call these minimizers quasi-optimal.We minimize the cost functionals using dynamic programming. Finding these minimizers is more computationally expensive than solving the original algebraic system. Nevertheless, from the insights provided by the analysis of the dynamic programming minima, we propose a heuristic construction of the elimination trees that has cost O(log(Ne log(Ne)), where N e is the number of elements in the mesh.We show that this heuristic ordering has similar computational cost to the quasi-optimal elimination trees found with dynamic programming and outperforms state-of-the-art alternatives in our numerical experiments
Discrete Elastic Inner Vector Spaces with Application in Time Series and Sequence Mining
This paper proposes a framework dedicated to the construction of what we call
discrete elastic inner product allowing one to embed sets of non-uniformly
sampled multivariate time series or sequences of varying lengths into inner
product space structures. This framework is based on a recursive definition
that covers the case of multiple embedded time elastic dimensions. We prove
that such inner products exist in our general framework and show how a simple
instance of this inner product class operates on some prospective applications,
while generalizing the Euclidean inner product. Classification experimentations
on time series and symbolic sequences datasets demonstrate the benefits that we
can expect by embedding time series or sequences into elastic inner spaces
rather than into classical Euclidean spaces. These experiments show good
accuracy when compared to the euclidean distance or even dynamic programming
algorithms while maintaining a linear algorithmic complexity at exploitation
stage, although a quadratic indexing phase beforehand is required.Comment: arXiv admin note: substantial text overlap with arXiv:1101.431
Dynamic programming for graphs on surfaces
We provide a framework for the design and analysis of dynamic
programming algorithms for surface-embedded graphs on n vertices
and branchwidth at most k. Our technique applies to general families
of problems where standard dynamic programming runs in 2O(k·log k).
Our approach combines tools from topological graph theory and
analytic combinatorics.Postprint (updated version
Dynamic Programming for Graphs on Surfaces
We provide a framework for the design and analysis of dynamic programming
algorithms for surface-embedded graphs on n vertices and branchwidth at most k.
Our technique applies to general families of problems where standard dynamic
programming runs in 2^{O(k log k)} n steps. Our approach combines tools from
topological graph theory and analytic combinatorics. In particular, we
introduce a new type of branch decomposition called "surface cut
decomposition", generalizing sphere cut decompositions of planar graphs
introduced by Seymour and Thomas, which has nice combinatorial properties.
Namely, the number of partial solutions that can be arranged on a surface cut
decomposition can be upper-bounded by the number of non-crossing partitions on
surfaces with boundary. It follows that partial solutions can be represented by
a single-exponential (in the branchwidth k) number of configurations. This
proves that, when applied on surface cut decompositions, dynamic programming
runs in 2^{O(k)} n steps. That way, we considerably extend the class of
problems that can be solved in running times with a single-exponential
dependence on branchwidth and unify/improve most previous results in this
direction.Comment: 28 pages, 3 figure
Fast Repeater Tree Construction
Repeaters are used during physical design of chips to improve the electrical and timing properties of interconnections. They are added along Steiner trees that connect root gates to sinks, creating repeater trees. Their construction became a crucial part of chip design. We present a new algorithm to solve the repeater tree construction problem. We first present an extensive version of the Repeater Tree Problem. Our problem formulation encapsulates most of the constraints that have been studied so far. We also consider several aspects for the first time, for example, slew dependent required arrival times at repeater tree sinks. The employed technology, the properties of available repeaters and metal wires, the shape of the chip, the temperature, the voltages, and many other factors highly influence the results of repeater tree construction. To take all this into account, we extensively preprocess the environment to extract parameters for our algorithms. We first present an algorithm for Steiner tree creation and prove that our algorithm is able to create timing-efficient as well as cost-efficient trees. Our algorithm is based on a delay model that accurately describes the timing that one can achieve after repeater insertion upfront. Next, we deal with the problem of adding repeaters to a given Steiner tree. The predominantly used algorithms to solve this problem use dynamic programming. However, they have several drawbacks. Firstly, potential repeater positions along the Steiner tree have to be chosen upfront. Secondly, the algorithms strictly follow the given Steiner tree and miss optimization opportunities. Finally, dynamic programming causes high running times. We present our new buffer insertion algorithm, Fast Buffering, that overcomes these limitations. It is able to produce results with similar quality to a dynamic programming approach but a much better running time. In addition, we also present improvements to the dynamic programming approach that allows us to push the quality at the expense of a high running time. We have implemented our algorithms as part of the BonnTools physical design optimization suite developed at the Research Institute for Discrete Mathematics in cooperation with IBM. Our implementation deals with all tedious details of a grown real-world chip optimization environment. We have created extensive experimental results on challenging real-world test cases provided by our cooperation partner. Our algorithm can solve about 5.7 million instances per hour
The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review
Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe
SamACO: variable sampling ant colony optimization algorithm for continuous optimization
An ant colony optimization (ACO) algorithm offers
algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution
constructions and to realize a pheromone laying-and-following
mechanism. Although ACO is first designed for solving discrete
(combinatorial) optimization problems, the ACO procedure is
also applicable to continuous optimization. This paper presents
a new way of extending ACO to solving continuous optimization
problems by focusing on continuous variable sampling as a key
to transforming ACO from discrete optimization to continuous
optimization. The proposed SamACO algorithm consists of three
major steps, i.e., the generation of candidate variable values for
selection, the ants’ solution construction, and the pheromone
update process. The distinct characteristics of SamACO are the
cooperation of a novel sampling method for discretizing the
continuous search space and an efficient incremental solution
construction method based on the sampled values. The performance
of SamACO is tested using continuous numerical functions
with unimodal and multimodal features. Compared with some
state-of-the-art algorithms, including traditional ant-based algorithms
and representative computational intelligence algorithms
for continuous optimization, the performance of SamACO is seen
competitive and promising
Multi-heuristic and game approaches in search problems of the graph theory
We consider in this paper the adaptation of heuristics used for programming
non-deterministic games to the problems of discrete optimization, in particular, some
heuristic methods of decision-making in various discrete optimization problems. The
object of each of these problems is programming anytime algorithms. Among the
problems solved in this paper, there are the classical traveling salesman problem and
some connected problems of minimization for nondeterministic finite automata.
Considered methods for solving these problems are constructed on the basis of special
combination of some heuristics, which belong to some different areas of the theory of
artificial intelligence. More precisely, we shall use some modifications of unfinished
branch-and-bound method; for the selecting immediate step using some heuristics, we
apply dynamic risk functions; simultaneously for the selection of coefficients of the
averaging-out, we also use genetic algorithms; and the reductive self-learning by the
same genetic methods is also used for the start of unfinished branch-and-bound method
again. This combination of heuristics represents a special approach to construction of
anytime-algorithms for the discrete optimization problems. This approach can be
considered as an alternative to application of methods of linear programming, and to
methods of multi-agent optimization, and also to neural networks
- …