Showing 1-25 of 4,256 resultsfor "Index Terms":Rank Constraint
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Sync-Rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and SDP Synchronization
We consider the classical problem of establishing a statistical ranking of a set of $n$ items given a set of inconsistent and incomplete pairwise comparisons between such items. Instantiations of this problem occur in numerous applications in data analysis (e.g., ranking teams in sports data), computer vision, and machine learning. We formulate the above problem of ranking with incomplete noisy ...Show More
Constraint Handling in Multiobjective Evolutionary Optimization
This paper proposes a constraint handling technique for multiobjective evolutionary algorithms based on an adaptive penalty function and a distance measure. These two functions vary dependent upon the objective function value and the sum of constraint violations of an individual. Through this design, the objective space is modified to account for the performance and constraint violation of each in...Show More
Tools for the robust analysis of error-driven ranking algorithms and their implications for modelling the child's acquisition of phonotactics
Error-driven ranking algorithms (EDRAs) perform a sequence of slight re-rankings of the constraint set triggered by mistakes on the incoming stream of data. The sequence of rankings entertained by the algorithm (and in particular the final ranking entertained at convergence) depends not only on the grammar the algorithm is trained on, but also on the specific way data are sampled from that grammar...Show More
This study proposes a new surrogate-assisted evolutionary algorithm, CHDE+ELDR, that combines CHDE, a constraint-handling evolutionary algorithm, and ELDR, a pairwise ranking surrogate model. The experiment compares CHDE+ELDR with CHDE without a surrogate model on 13 constrained optimization benchmark problems. The experimental results show that CHDE+ELDR significantly outperforms CHDE without a s...Show More
Rank-Constrained Separable Semidefinite Programming With Applications to Optimal Beamforming
Consider a downlink communication system where multiantenna base stations transmit independent data streams to decentralized single-antenna users over a common frequency band. The goal of the base stations is to jointly adjust the beamforming vectors to minimize the transmission powers while ensuring the signal-to-interference-noise ratio requirement of each user within the system. At the same tim...Show More
Two novel numerically efficient approaches are proposed for maximum capacity designs in multiple-input multiple-output (MIMO) systems under the practical per-antenna power constraint. One is an explicit solution (ES) when the rank of the channel matrix is equal to the number of transmit antennas. And the other is an efficient iterative approach (EIA). The ES is optimum and the EIA is nearly optimu...Show More
Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration
In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In general, HSIs are not only assumed to lie in a low-rank subspace from the spectral perspective but also assumed to be piecewise smooth in the spatial dimension. The proposed method integrates the nuclear norm, TV regulariz...Show More
Hybrid Stochastic Ranking for Constrained Optimization
Muhammad Asif Jan;Muhammad Sagheer;Hidayat Ullah Khan;Muhammad Irfan Uddin;Rashida Adeeb Khanum;Marwan Mahmoud;Ikramullah;Noor Mast
Teaching learning based optimization (TLBO) algorithm is a distinguished nature-inspired population-based meta-heuristic, which is basically designed for unconstrained optimization. TLBO mimics teaching learning process through which learners acquire knowledge from their teachers, and improve their results/grades, accordingly. Stochastic ranking (SR) is a constrained handling technique (CHT), whic...Show More
Recovering the missing components in a large noisy low-rank matrix: application to SFM
In computer vision, it is common to require operations on matrices with "missing data," for example, because of occlusion or tracking failures in the Structure from Motion (SFM) problem. Such a problem can be tackled, allowing the recovery of the missing values, if the matrix should be of low rank (when noise free). The filling in of missing values is known as imputation. Imputation can also be ap...Show More
Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications
Tae-Hyun Oh;Yu-Wing Tai;Jean-Charles Bazin;Hyeongwoo Kim;In So Kweon
Robust Principal Component Analysis (RPCA) via rank minimization is a powerful tool for recovering underlying low-rank structure of clean data corrupted with sparse noise/outliers. In many low-level vision problems, not only it is known that the underlying structure of clean data is low-rank, but the exact rank of clean data is also known. Yet, when applying conventional rank minimization for thos...Show More
How to balance diversity and convergence as well as constraint violation and objective function in Constrained Optimization Problems (COPs) is an important and key issue when designing a new Constrained Optimization Evolutionary Algorithms (COEAs). C2oDE set a good example on this point, and obtains promising results. In C2oDE, the two Constraint Handling Techniques (CHTs) are adopted fixed in two...Show More
Low-Rank And Sparse Tensor Representation For Multi-View Subspace Clustering
Shuqin Wang;Yongyong Chen;Yigang Ce;Linna Zhang;Viacheslav Voronin
2021 IEEE International Conference on Image Processing (ICIP)
Year: 2021 | Conference Paper |
Cited by: Papers (7)
Learning an effective affinity matrix as the input of spectral clustering to achieve promising multi-view clustering is a key issue of subspace clustering. In this paper, we propose a low-rank and sparse tensor representation (LRSTR) method that learns the affinity matrix through a self-representation tensor and retains the similarity information of the view dimensions for multi-view subspace clus...Show More
Is stochastic ranking really better than Feasibility Rules for constraint handling in Evolutionary Algorithms?
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC)
Year: 2009 | Conference Paper |
Cited by: Papers (6)
Evolutionary algorithms have been widely used to solve difficult constrained optimization problems. However, evolutionary algorithms are intrinsically unconstrained optimization techniques. Constraint handling is mostly incorporated additionally and its choice has great bearing on the quality of the solution. Stochastic ranking was introduced as an improvement over feasibility rules for handling c...Show More
Novel MMSE precoder and decoder designs subject to per-antenna power constraint for uplink multiuser MIMO systems
2009 3rd International Conference on Signal Processing and Communication Systems
Year: 2009 | Conference Paper |
Cited by: Papers (5)
Two complementary numerical approaches, the generalized iterative approach (GIA) and the transmit covariance optimization approach (TCOA) are proposed for jointly designing the minimum mean square error (MMSE) precoders and decoders in uplink multiuser multiple-input-multiple-output (MIMO) systems with a per-antenna power constraint. The TCOA always give optimum solution but works only when the so...Show More
Necessary and sufficient conditions for success of the nuclear norm heuristic for rank minimization
2008 47th IEEE Conference on Decision and Control
Year: 2008 | Conference Paper |
Minimizing the rank of a matrix subject to constraints is a challenging is a challenging problem that arises in many control applications including controller design, realization theory and model reduction. This class of optimization problems, known as rank minimization, is NP-HARD, and for most practical problems there are no efficient algorithms that yield exact solutions. A popular heuristic al...Show More
An Improved C2oDE based on Stochastic Ranking: A Further Study
2021 China Automation Congress (CAC)
Year: 2021 | Conference Paper |
Constraint Handling Techniques plays an important role when solving Constrained Optimization Problems. In the previous paper, an improved constrained composite differential evolution (C2oDE) based on stochastic ranking (SR) is proposed with the aim to check if some other combinations of CHTs can be more effective during the two phases of C2oDE. This paper further studies the effect of the selectio...Show More
A weak maximum principle for optimal control problems with mixed constraints under a constant rank condition
Necessary optimality conditions for optimal control problems with mixed state-control equality constraints are obtained. The necessary conditions are given in the form of a weak maximum principle and are obtained under (i) a new regularity condition for problems with mixed linear equality constraints and (ii) a constant rank type condition for the general non-linear case. Some instances of problem...Show More
An Annealing Stochastic Ranking Mechanism for Constrained Evolutionary Optimization
2016 International Conference on Information System and Artificial Intelligence (ISAI)
Year: 2016 | Conference Paper |
Cited by: Papers (3)
Stochastic ranking is a promising constraint handling method for constrained evolutionary optimization problems. However, the method has some limitations when comparing two given individuals during the evolution. In order to find the optimal feasible solution, the promising infeasible individual is supposed to have an opportunity to survive if at least one of two compared individuals is infeasible...Show More
Locally Structured Low-Rank MR Image Reconstruction using Submatrix Constraints
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Image reconstruction methods based on structured low-rank matrix completion have drawn growing interest in magnetic resonance imaging. In this work, we propose a locally structured low-rank image reconstruction method which imposes low-rank constraints on submatrices of the Hankel structured k-space data matrix. Simulation experiments based on numerical phantoms and experimental data demonstrated ...Show More
An Evolution Strategy with stochastic ranking for solving reactive power optimization
2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS)
Year: 2009 | Conference Paper |
Cited by: Papers (1)
This paper presents an algorithm for solving reactive power optimization problem through the application of Evolution Strategy (ES) with stochastic ranking. In order to better improve the optimization performance and practicality, the coding method for integer data of transformer tap position is designed deliberately and the self-adaptive optimization termination condition based on variance is als...Show More
A solution to the singularity problem of a non-redundant robot is proposed by reformulating the inverse kinematic problem as a constraint optimization problem. The main idea is to allow a cartesian error in a given subspace in the vicinity of a singularity and to minimize this error subject to operational constraints such as maximum motor speeds. As a result, in every sampling instant a series of ...Show More
Better Feature Tracking through Subspace Constraints
2014 IEEE Conference on Computer Vision and Pattern Recognition
Year: 2014 | Conference Paper |
Cited by: Papers (9)
Feature tracking in video is a crucial task in computer vision. Usually, the tracking problem is handled one feature at a time, using a single-feature tracker like the Kanade-Lucas-Tomasi algorithm, or one of its derivatives. While this approach works quite well when dealing with high-quality video and "strong" features, it often falters when faced with dark and noisy video containing low-quality ...Show More
A nullspace analysis of the nuclear norm heuristic for rank minimization
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2010 | Conference Paper |
Cited by: Papers (18)
The problem of minimizing the rank of a matrix subject to linear equality constraints arises in applications in machine learning, dimensionality reduction, and control theory, and is known to be NP-hard. A popular heuristic minimizes the nuclear norm (sum of the singular values) of the matrix instead of the rank, and was recently shown to give an exact solution in several scenarios. In this paper,...Show More
Selecting Search Strategy in Constraint Solvers using Bayesian Optimization
2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI)
Year: 2024 | Conference Paper |
In the field of constraint programming, selecting the most effective search strategy for a new problem is a complex task. Despite the existence of numerous autonomous search strategies, the effectiveness of a strategy is highly problem-specific and no single strategy can universally excel. Therefore, for the solver's developers, it is difficult to find a good default strategy working across many p...Show More
An Effective Web Service Ranking Method via Exploring User Behavior
Service-oriented computing and Web services are becoming more and more popular, enabling organizations to use the Web as a market for selling their own Web services and consuming existing Web services from others. Nevertheless, with the increasing adoption and presence of Web services, it becomes more difficult to find the most appropriate Web service that satisfies both users' functional and nonf...Show More
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