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Relevant bibliographies by topics / Orthogonality constraint

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Academic literature on the topic 'Orthogonality constraint'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Orthogonality constraint"

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Kanzow, Christian, Andreas B. Raharja, and Alexandra Schwartz. "An Augmented Lagrangian Method for Cardinality-Constrained Optimization Problems." Journal of Optimization Theory and Applications 189, no. 3 (2021): 793–813. http://dx.doi.org/10.1007/s10957-021-01854-7.

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AbstractA reformulation of cardinality-constrained optimization problems into continuous nonlinear optimization problems with an orthogonality-type constraint has gained some popularity during the last few years. Due to the special structure of the constraints, the reformulation violates many standard assumptions and therefore is often solved using specialized algorithms. In contrast to this, we investigate the viability of using a standard safeguarded multiplier penalty method without any problem-tailored modifications to solve the reformulated problem. We prove global convergence towards an

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Zhu, Hong, Li-Zhi Liao, and Michael K. Ng. "Multi-Instance Dimensionality Reduction via Sparsity and Orthogonality." Neural Computation 30, no. 12 (2018): 3281–308. http://dx.doi.org/10.1162/neco_a_01140.

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We study a multi-instance (MI) learning dimensionality-reduction algorithm through sparsity and orthogonality, which is especially useful for high-dimensional MI data sets. We develop a novel algorithm to handle both sparsity and orthogonality constraints that existing methods do not handle well simultaneously. Our main idea is to formulate an optimization problem where the sparse term appears in the objective function and the orthogonality term is formed as a constraint. The resulting optimization problem can be solved by using approximate augmented Lagrangian iterations as the outer loop and

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Takahashi, Kosuke, Shohei Nobuhara, and Takashi Matsuyama. "Mirror-based Camera Pose Estimation Using an Orthogonality Constraint." IPSJ Transactions on Computer Vision and Applications 8 (2016): 11–19. http://dx.doi.org/10.2197/ipsjtcva.8.11.

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Xu, Feng, David Diner, Oleg Dubovik, and Yoav Schechner. "A Correlated Multi-Pixel Inversion Approach for Aerosol Remote Sensing." Remote Sensing 11, no. 7 (2019): 746. http://dx.doi.org/10.3390/rs11070746.

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Aerosol retrieval algorithms used in conjunction with remote sensing are subject to ill-posedness. To mitigate non-uniqueness, extra constraints (in addition to observations) are valuable for stabilizing the inversion process. This paper focuses on the imposition of an empirical correlation constraint on the retrieved aerosol parameters. This constraint reflects the empirical dependency between different aerosol parameters, thereby reducing the number of degrees of freedom and enabling accelerated computation of the radiation fields associated with neighboring pixels. A cross-pixel constraint

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Zhao, Bin, and Jia Yang. "A Low Complexity Robust Adaptive Beamforming Method with Orthogonality Constraint." Journal of Physics: Conference Series 1419 (December 2019): 012012. http://dx.doi.org/10.1088/1742-6596/1419/1/012012.

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Szekeres, Zsolt, and Péter R. Surján. "Direct determination of fragment localized molecular orbitals and the orthogonality constraint." Chemical Physics Letters 369, no. 1-2 (2003): 125–30. http://dx.doi.org/10.1016/s0009-2614(02)01988-7.

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Cassam-Chenaï, Patrick, and Vitaly Rassolov. "The electronic mean field configuration interaction method: III – the p-orthogonality constraint." Chemical Physics Letters 487, no. 1-3 (2010): 147–52. http://dx.doi.org/10.1016/j.cplett.2010.01.033.

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Yin, Deyu, Jingbin Liu, Teng Wu, Keke Liu, Juha Hyyppä, and Ruizhi Chen. "Extrinsic Calibration of 2D Laser Rangefinders Using an Existing Cuboid-Shaped Corridor as the Reference." Sensors 18, no. 12 (2018): 4371. http://dx.doi.org/10.3390/s18124371.

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Laser rangefinders (LRFs) are widely used in autonomous systems for indoor positioning and mobile mapping through the simultaneous localization and mapping (SLAM) approach. The extrinsic parameters of multiple LRFs need to be determined, and they are one of the key factors impacting system performance. This study presents an extrinsic calibration method of multiple LRFs that requires neither extra calibration sensors nor special artificial reference landmarks. Instead, it uses a naturally existing cuboid-shaped corridor as the calibration reference, and it hence needs no additional cost. The p

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Zhang, Ye, Tenglong Yu, and Wenwu Wang. "An Analysis Dictionary Learning Algorithm under a Noisy Data Model with Orthogonality Constraint." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/852978.

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Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorith

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Datta, B. N., S. Deng, V. O. Sokolov, and D. R. Sarkissian. "An optimization technique for damped model updating with measured data satisfying quadratic orthogonality constraint." Mechanical Systems and Signal Processing 23, no. 6 (2009): 1759–72. http://dx.doi.org/10.1016/j.ymssp.2008.07.017.

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Dissertations / Theses on the topic "Orthogonality constraint"

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Khatavkar, Rohan. "Sparse and orthogonal singular value decomposition." Kansas State University, 2013. http://hdl.handle.net/2097/15992.

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Master of Science<br>Department of Statistics<br>Kun Chen<br>The singular value decomposition (SVD) is a commonly used matrix factorization technique in statistics, and it is very e ective in revealing many low-dimensional structures in a noisy data matrix or a coe cient matrix of a statistical model. In particular, it is often desirable to obtain a sparse SVD, i.e., only a few singular values are nonzero and their corresponding left and right singular vectors are also sparse. However, in several existing methods for sparse SVD estimation, the exact orthogonality among the singular vector

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Zhu, Hong. "Solving optimization problems with generalized orthogonality constraints." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/341.

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This thesis focuses on optimization problems with generalized orthogonality constraints, which may also contains linear equality constraints. These problems appear in many areas, such as machine learning, signal processing, computer vision and so on.;Many problems in this form are NP hard. One challenge posed by generalized orthogonality constraints is local minimizers loaded by nonconvex constraints. More-over, the generalized orthogonality constraints are numerically expensive to preserve during iterations.;This thesis is mainly divided into two parts. The first part is focused on solving ge

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"Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence." 2016. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1292697.

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Book chapters on the topic "Orthogonality constraint"

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Plumbley, Mark D. "Lie Group Methods for Optimization with Orthogonality Constraints." In Independent Component Analysis and Blind Signal Separation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30110-3_157.

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Liu, Xiuwen, and Anuj Srivastava. "Stochastic Search for Optimal Linear Representations of Images on Spaces with Orthogonality Constraints." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45063-4_1.

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Glushkov, V. N., and X. Assfeld. "Orthogonality-constrained Hartree–Fock and perturbation theory for high-spin open-shell excited states." In Péter R. Surján. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-49825-5_23.

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Koldovský, Zbyněk, Petr Tichavský, and Nobutaka Ono. "Orthogonally-Constrained Extraction of Independent Non-Gaussian Component from Non-Gaussian Background Without ICA." In Latent Variable Analysis and Signal Separation. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93764-9_16.

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Halpin, Terry. "Supplementing UML with Concepts from ORM." In Unified Modeling Language. IGI Global, 2001. http://dx.doi.org/10.4018/978-1-930708-05-1.ch011.

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The Unified Modeling Language (UML) is useful for designing object-oriented code, but is less suitable for conceptual data analysis. Its process-centric use-cases provide an inadequate basis for specifying data-centric class diagrams, and the UML graphical language suffers from incompleteness, inconsistency and unnecessary complexity. For example, multiplicity constraints can lead to unexpected problems when extended to n-ary associations, the constraint primitives are not optimized for orthogonality or expressibility, and the graphical language does not lend itself readily to verbalization and multiple instantiation for validating models with domain experts. This chapter examines some of these defects, and shows how to compensate for them by augmenting UML with concepts and techniques from the Object Role Modeling (ORM) approach. It highlights the potential of “data use cases” for seeding the data model, using verbalization of facts and rules with positive and negative examples to facilitate validation of business rules. The following approaches are suggested as possible ways to exploit the benefits of fact-orientation: use ORM for the conceptual analysis then map the ORM model to UML; use UML supplemented by informal population diagrams and user-defined constraints; enhance the UML metamodel to better support business rules.

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Scarani, Valerio. "The Quest for Device-Independent Quantum Principles." In Bell Nonlocality. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198788416.003.0010.

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The fact that the no-signaling set is larger than the quantum set suggests that the excess regions could be cut off by invoking additional constraints. In this chapter, we present some of the constraints that have been proposed as “physical principles”: information causality, macroscopic locality and local orthogonality. All three capture interesting features of the quantum set but fail to single ti out exactly.

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Simões, Luis M. C., József Farkas, and Károly Jármai. "Optimization of an Orthogonally Stiffened Plate Considering Fatigue Constraints." In Design, Fabrication and Economy of Welded Structures. Elsevier, 2008. http://dx.doi.org/10.1533/9781782420484.1.27.

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Conference papers on the topic "Orthogonality constraint"

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AlSahlani, Assaad, and Ranjan Mukherjee. "Dynamics Simulation of a Circular Membrane With an Eccentric Circular Areal Constraint." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48489.

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We investigate the dynamics of a circular membrane with an eccentric circular areal constraint under arbitrary initial conditions. The membrane is assumed to be fixed at its outer boundary and the constraint is assumed to impose zero displacement over a circular area of the membrane. The symmetric and antisymmetric modes of vibration of the membrane are derived and their orthogonality properties are established. Using the orthogonality conditions established in this paper, the complete solution to the constrained vibratory motion of the membrane is determined for arbitrary initial conditions.

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El Gheche, Mireille, Giovanni Chierchia, and Pascal Frossard. "Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683286.

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Gabrielson, Ben, M. A. B. S. Akhonda, Zois Boukouvalas, Seung-Jun Kim, and Tulay Adali. "ICA with Orthogonality Constraint: Identifiability And A New Efficient Algorithm." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9415059.

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Takahashi, K., S. Nobuhara, and T. Matsuyama. "A new mirror-based extrinsic camera calibration using an orthogonality constraint." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247783.

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Sliwa, Tadeusz, Yvon Voisin, and Alain Diou. "Adaptivity with near-orthogonality constraint for high compression rates in lifting scheme framework." In Optical Science and Technology, SPIE's 48th Annual Meeting, edited by Mark S. Schmalz. SPIE, 2004. http://dx.doi.org/10.1117/12.505488.

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Lin Wang, Ting Li, Xiongjun Fu, Lumin Shi, and Meiguo Gao. "Radar waveform design under the constraint of auto-correlation, orthogonality and doppler tolerance." In IET International Radar Conference 2013. Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.0529.

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Karami, Mohammad, Luigi Carassale, Horia Hangan, and Maryam Refan. "Coherent Structures of Tornado-Like Vortices." In ASME 2018 5th Joint US-European Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/fedsm2018-83152.

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Tornado-like vortices have distinctive characteristics that make them significantly different from atmospheric boundary flows. These dynamic characteristics are highly turbulent and unsteady which make them very complex. In this paper, it is tried to identify these dominant patterns, known as coherent structures, out of tornado-like vortex. These vortices are experimentally simulated in a 1/11 scaled model of WindEEE Dome at Western University. Coherent structures provide simplified but physical understanding of the flow. The classical method for extraction of coherent structures is proper ort

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Li, Ziwei, Gengyu Lyu, and Songhe Feng. "Partial Multi-Label Learning via Multi-Subspace Representation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/362.

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Partial Multi-Label Learning (PML) aims to learn from the training data where each instance is associated with a set of candidate labels, among which only a part of them are relevant. Existing PML methods mainly focus on label disambiguation, while they lack the consideration of noise in the feature space. To tackle the problem, we propose a novel framework named partial multi-label learning via MUlti-SubspacE Representation (MUSER), where the redundant labels together with noisy features are jointly taken into consideration during the training process. Specifically, we first decompose the ori

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Strömberg, Niclas. "Reliability Based Design Optimization by Using a SLP Approach and Radial Basis Function Networks." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59522.

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In this paper reliability based design optimization by using radial basis function networks (RBFN) as surrogate models is presented. The RBFN are treated as regression models. By taking the center points equal to the sampling points an interpolation is obtained. The bias of the network is taken to be known a priori or posteriori. In the latter case, the well-known orthogonality constraint between the weights of the RBFN and the polynomial basis functions of the bias is adopted. The optimization is performed by using a first order reliability method (FORM)-based sequential linear programming (S

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Zhang, Aston, Alvin Chan, Yi Tay, et al. "On Orthogonality Constraints for Transformers." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-short.48.

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