9,938 research outputs found
A Heuristic Approach to the Consecutive Ones Submatrix Problem
أعطيت مصفوفة (0،1)، تم اقتراح مسألة المصفوفة الجزئية ذات الواحدات المتعاقبة والتي تهدف إلى إيجاد تبديل للأعمدة التي تزيد من عدد الأعمدة التي تحتوي معًا على قالب واحد فقط من الواحدات المتعاقبة في كل صف. سيتم اقتراح اسلوب الاستدلال لحل المسألة. كما سيتم دراسة مسألة تقليل القوالب المتتالية ذات الصلة بمسألة المصفوفة الجزئية ذات الواحدات المتعاقبة. تم اقتراح اجراء جديد لتحسين طريقة إدراج العمود. يتم بعد ذلك تقييم مصفوفات العالم الحقيقي ومصفوفات متولدة عشوائيًا من مسألة غطاء المجموعة و تعرض النتائج الحسابية.Given a matrix, the Consecutive Ones Submatrix (C1S) problem which aims to find the permutation of columns that maximizes the number of columns having together only one block of consecutive ones in each row is considered here. A heuristic approach will be suggested to solve the problem. Also, the Consecutive Blocks Minimization (CBM) problem which is related to the consecutive ones submatrix will be considered. The new procedure is proposed to improve the column insertion approach. Then real world and random matrices from the set covering problem will be evaluated and computational results will be highlighted
Sequential optimization of strip bending process using multiquadric radial basis function surrogate models
Surrogate models are used within the sequential optimization strategy for forming processes. A sequential improvement (SI) scheme is used to refine the surrogate model in the optimal region. One of the popular surrogate modeling methods for SI is Kriging. However, the global response of Kriging models deteriorates in some cases due to local model refinement within SI. This may be problematic for multimodal optimization problems and for other applications where correct prediction of the global response is needed. In this paper the deteriorating global behavior of the Kriging surrogate modeling technique is shown for a model of a strip bending process. It is shown that a Radial Basis Function (RBF) surrogate model with Multiquadric (MQ) basis functions performs equally well in terms of optimization efficiency and better in terms of global predictive accuracy. The local point density is taken into account in the model formulatio
Convex Relaxations for Permutation Problems
Seriation seeks to reconstruct a linear order between variables using
unsorted, pairwise similarity information. It has direct applications in
archeology and shotgun gene sequencing for example. We write seriation as an
optimization problem by proving the equivalence between the seriation and
combinatorial 2-SUM problems on similarity matrices (2-SUM is a quadratic
minimization problem over permutations). The seriation problem can be solved
exactly by a spectral algorithm in the noiseless case and we derive several
convex relaxations for 2-SUM to improve the robustness of seriation solutions
in noisy settings. These convex relaxations also allow us to impose structural
constraints on the solution, hence solve semi-supervised seriation problems. We
derive new approximation bounds for some of these relaxations and present
numerical experiments on archeological data, Markov chains and DNA assembly
from shotgun gene sequencing data.Comment: Final journal version, a few typos and references fixe
Activity recognition from videos with parallel hypergraph matching on GPUs
In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently used to recognize
complex and often non-rigid patterns in computer vision, either through graph
matching or point-set matching with graphs. Most formulations resort to the
minimization of a difficult discrete energy function mixing geometric or
structural terms with data attached terms involving appearance features.
Traditional methods solve this minimization problem approximately, for instance
with spectral techniques.
In this work, instead of solving the problem approximatively, the exact
solution for the optimal assignment is calculated in parallel on GPUs. The
graphical structure is simplified and regularized, which allows to derive an
efficient recursive minimization algorithm. The algorithm distributes
subproblems over the calculation units of a GPU, which solves them in parallel,
allowing the system to run faster than real-time on medium-end GPUs
Wideband Super-resolution Imaging in Radio Interferometry via Low Rankness and Joint Average Sparsity Models (HyperSARA)
We propose a new approach within the versatile framework of convex
optimization to solve the radio-interferometric wideband imaging problem. Our
approach, dubbed HyperSARA, solves a sequence of weighted nuclear norm and l21
minimization problems promoting low rankness and joint average sparsity of the
wideband model cube. On the one hand, enforcing low rankness enhances the
overall resolution of the reconstructed model cube by exploiting the
correlation between the different channels. On the other hand, promoting joint
average sparsity improves the overall sensitivity by rejecting artefacts
present on the different channels. An adaptive Preconditioned Primal-Dual
algorithm is adopted to solve the minimization problem. The algorithmic
structure is highly scalable to large data sets and allows for imaging in the
presence of unknown noise levels and calibration errors. We showcase the
superior performance of the proposed approach, reflected in high-resolution
images on simulations and real VLA observations with respect to single channel
imaging and the CLEAN-based wideband imaging algorithm in the WSCLEAN software.
Our MATLAB code is available online on GITHUB
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model
In this work, we propose a novel procedure for video super-resolution, that
is the recovery of a sequence of high-resolution images from its low-resolution
counterpart. Our approach is based on a "sequential" model (i.e., each
high-resolution frame is supposed to be a displaced version of the preceding
one) and considers the use of sparsity-enforcing priors. Both the recovery of
the high-resolution images and the motion fields relating them is tackled. This
leads to a large-dimensional, non-convex and non-smooth problem. We propose an
algorithmic framework to address the latter. Our approach relies on fast
gradient evaluation methods and modern optimization techniques for
non-differentiable/non-convex problems. Unlike some other previous works, we
show that there exists a provably-convergent method with a complexity linear in
the problem dimensions. We assess the proposed optimization method on {several
video benchmarks and emphasize its good performance with respect to the state
of the art.}Comment: 37 pages, SIAM Journal on Imaging Sciences, 201
Approximating Weighted Duo-Preservation in Comparative Genomics
Motivated by comparative genomics, Chen et al. [9] introduced the Maximum
Duo-preservation String Mapping (MDSM) problem in which we are given two
strings and from the same alphabet and the goal is to find a
mapping between them so as to maximize the number of duos preserved. A
duo is any two consecutive characters in a string and it is preserved in the
mapping if its two consecutive characters in are mapped to same two
consecutive characters in . The MDSM problem is known to be NP-hard and
there are approximation algorithms for this problem [3, 5, 13], but all of them
consider only the "unweighted" version of the problem in the sense that a duo
from is preserved by mapping to any same duo in regardless of their
positions in the respective strings. However, it is well-desired in comparative
genomics to find mappings that consider preserving duos that are "closer" to
each other under some distance measure [19]. In this paper, we introduce a
generalized version of the problem, called the Maximum-Weight Duo-preservation
String Mapping (MWDSM) problem that captures both duos-preservation and
duos-distance measures in the sense that mapping a duo from to each
preserved duo in has a weight, indicating the "closeness" of the two
duos. The objective of the MWDSM problem is to find a mapping so as to maximize
the total weight of preserved duos. In this paper, we give a polynomial-time
6-approximation algorithm for this problem.Comment: Appeared in proceedings of the 23rd International Computing and
Combinatorics Conference (COCOON 2017
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