Relevant bibliographies by topics / First Order Optimization Methods

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'First Order Optimization Methods'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "First Order Optimization Methods"

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Dvurechensky, Pavel, Shimrit Shtern, and Mathias Staudigl. "First-Order Methods for Convex Optimization." EURO Journal on Computational Optimization 9 (2021): 100015. http://dx.doi.org/10.1016/j.ejco.2021.100015.

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Ershov, M. D. "First-Order Optimization Methods in Machine Learning." INFORMACIONNYE TEHNOLOGII 25, no. 11 (2019): 662–69. http://dx.doi.org/10.17587/it.25.662-669.

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Lu, Zhaosong, and Sanyou Mei. "First-Order Penalty Methods for Bilevel Optimization." SIAM Journal on Optimization 34, no. 2 (2024): 1937–69. http://dx.doi.org/10.1137/23m1566753.

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Chiralaksanakul, Anukal, and Sankaran Mahadevan. "First-Order Approximation Methods in Reliability-Based Design Optimization." Journal of Mechanical Design 127, no. 5 (2004): 851–57. http://dx.doi.org/10.1115/1.1899691.

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Efficiency of reliability-based design optimization (RBDO) methods is a critical criterion as to whether they are viable for real-world problems. Early RBDO methods are thus based primarily on the first-order reliability method (FORM) due to its efficiency. Recently, several first-order RBDO methods have been proposed, and their efficiency is significantly improved through problem reformulation and/or the use of inverse FORM. Our goal is to present these RBDO methods from a mathematical optimization perspective by formalizing FORM, inverse FORM, and associated RBDO reformulations. Through the
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Gonzaga, Clóvis C., and Elizabeth W. Karas. "COMPLEXITY OF FIRST-ORDER METHODS FOR DIFFERENTIABLE CONVEX OPTIMIZATION." Pesquisa Operacional 34, no. 3 (2014): 395–419. http://dx.doi.org/10.1590/0101-7438.2014.034.03.0395.

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Teboulle, Marc. "A simplified view of first order methods for optimization." Mathematical Programming 170, no. 1 (2018): 67–96. http://dx.doi.org/10.1007/s10107-018-1284-2.

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Dambrine, M., Ch Dossal, B. Puig, and A. Rondepierre. "Stochastic Differential Equations for Modeling First Order Optimization Methods." SIAM Journal on Optimization 34, no. 2 (2024): 1402–26. http://dx.doi.org/10.1137/21m1435665.

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David, Villacís. "First Order Methods for High Resolution Image Denoising." Latin-American Journal of Computing 4, no. 3 (2017): 37–42. https://doi.org/10.5281/zenodo.5764177.

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In this paper we are interested in comparing the performance of some of the most relevant first order non-smooth optimization methods applied to the Rudin, Osher and Fatemi(ROF) Image Denoising Model and a Primal-Dual Chambolle-Pock Image Denoising Model. Because of the properties of the resulting numerical schemes it is possible to handle these computations pixel wise, allowing implementations based on parallel paradigms which are helpful in the context of high resolution imaging.
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Savchuk, Oleg S., Alexander A. Titov, Fedor Sergeevich Stonyakin, and Mohammad S. Alkousa. "Adaptive first-order methods for relatively strongly convex optimization problems." Computer Research and Modeling 14, no. 2 (2022): 445–72. http://dx.doi.org/10.20537/2076-7633-2022-14-2-445-472.

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Lu, Haihao, Robert M. Freund, and Yurii Nesterov. "Relatively Smooth Convex Optimization by First-Order Methods, and Applications." SIAM Journal on Optimization 28, no. 1 (2018): 333–54. http://dx.doi.org/10.1137/16m1099546.

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Dissertations / Theses on the topic "First Order Optimization Methods"

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Lan, Guanghui. "Convex optimization under inexact first-order information." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29732.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Arkadi Nemirovski; Committee Co-Chair: Alexander Shapiro; Committee Co-Chair: Renato D. C. Monteiro; Committee Member: Anatoli Jouditski; Committee Member: Shabbir Ahmed. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Barré, Mathieu. "Worst-case analysis of efficient first-order methods." Electronic Thesis or Diss., Université Paris sciences et lettres, 2021. http://www.theses.fr/2021UPSLE064.

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De nombreuses applications modernes reposent sur la résolution de problèmes d’optimisations (par exemple, en biologie numérique, en mécanique, en finance), faisant des méthodes d’optimisation des outils essentiels dans de nombreux domaines scientifiques. Apporter des garanties sur le comportement de ces méthodes constitue donc un axe de recherche important. Une façon classique d’analyser un algorithme d’optimisation consiste à étudier son comportement dans le pire cas. C'est-à-dire, donner des garanties sur son comportement (par exemple sa vitesse de convergence) qui soient indépendantes de la
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Silveti, Falls Antonio. "First-order noneuclidean splitting methods for large-scale optimization : deterministic and stochastic algorithms." Thesis, Normandie, 2021. http://www.theses.fr/2021NORMC204.

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Dans ce travail, nous développons et examinons deux nouveaux algorithmes d'éclatement du premier ordre pour résoudre des problèmes d'optimisation composites à grande échelle dans des espaces à dimensions infinies. Ces problèmes sont au coeur de nombres de domaines scientifiques et d'ingénierie, en particulier la science des données et l'imagerie. Notre travail est axé sur l'assouplissement des hypothèses de régularité de Lipschitz généralement requises par les algorithmes de fractionnement du premier ordre en remplaçant l'énergie euclidienne par une divergence de Bregman. Ces développements pe
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Khirirat, Sarit. "Randomized first-order methods for convex optimization : Improved convergence rate bounds and experimental evaluations." Thesis, KTH, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214697.

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Huge-scale optimization problems appear in several applications ranging frommachine learning over large data sets to distributed model predictive control.Classical optimization algorithms struggle to handle these large-scale computations,and recently, a number of randomized rst-order methods that are simpleto implement and have small per-iteration cost have been proposed. However,optimal step size selections and corresponding convergence rates of many randomizedrst-order methods were still unknown. In this thesis, we hence deriveconvergence rate results for several randomized rst-order methods
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Van, Mai Vien. "Large-Scale Optimization With Machine Learning Applications." Licentiate thesis, KTH, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263147.

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This thesis aims at developing efficient algorithms for solving some fundamental engineering problems in data science and machine learning. We investigate a variety of acceleration techniques for improving the convergence times of optimization algorithms.  First, we investigate how problem structure can be exploited to accelerate the solution of highly structured problems such as generalized eigenvalue and elastic net regression. We then consider Anderson acceleration, a generic and parameter-free extrapolation scheme, and show how it can be adapted to accelerate practical convergence of proxi
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He, Niao. "Saddle point techniques in convex composite and error-in-measurement optimization." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54400.

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This dissertation aims to develop efficient algorithms with improved scalability and stability properties for large-scale optimization and optimization under uncertainty, and to bridge some of the gaps between modern optimization theories and recent applications emerging in the Big Data environment. To this end, the dissertation is dedicated to two important subjects -- i) Large-scale Convex Composite Optimization and ii) Error-in-Measurement Optimization. In spite of the different natures of these two topics, the common denominator, to be presented, lies in their accommodation for systematic
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Ghadimi, Euhanna. "Accelerating Convergence of Large-scale Optimization Algorithms." Doctoral thesis, KTH, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-162377.

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Several recent engineering applications in multi-agent systems, communication networks, and machine learning deal with decision problems that can be formulated as optimization problems. For many of these problems, new constraints limit the usefulness of traditional optimization algorithms. In some cases, the problem size is much larger than what can be conveniently dealt with using standard solvers. In other cases, the problems have to be solved in a distributed manner by several decision-makers with limited computational and communication resources. By exploiting problem structure, however, i
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Sforni, Lorenzo. "A First-Order Closed-loop Methodology for Nonlinear Optimal Control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21429/.

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This thesis is focused on state-of-art numerical optimization methods for nonlinear (discrete-time) optimal control. These challenging problems arise when dealing with complex tasks for autonomous systems (e.g. vehicles or robots) which require the generation of a trajectory that satisfies the system dynamics and, possibly, input and state constraints due to, e.g, actuator limits or safety region of operation. A general formulation is proposed that allows the implementation of different descent optimization algorithms on optimal control problems exploiting the beneficial effects of state f
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Clark, Patrick Ryan. "Reliability-Based Topology Optimization with Analytic Sensitivities." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78665.

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It is a common practice when designing a system to apply safety factors to the critical failure load or event. These safety factors provide a buffer against failure due to the random or un-modeled behavior, which may lead the system to exceed these limits. However these safety factors are not directly related to the likelihood of a failure event occurring. If the safety factors are poorly chosen, the system may fail unexpectedly or it may have a design which is too conservative. Reliability-Based Design Optimization (RBDO) is an alternative approach which directly considers the likelihood of f
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Alli-Oke, Razak Olusegun. "Robustness and optimization in anti-windup control." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/robustness-and-optimization-in-antiwindup-control(8b98c920-90c3-4fbc-95a8-0cc7ae2a607a).html.

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This thesis is broadly concerned with online-optimizing anti-windup control. These are control structures that implement some online-optimization routines to compensate for the windup effects in constrained control systems. The first part of this thesis examines a general framework for analyzing robust preservation in anti-windup control systems. This framework - the robust Kalman conjecture - is defined for the robust Lur’e problem. This part of the thesis verifies this conjecture for first-order plants perturbed by various norm-bounded unstructured uncertainties. Integral quadratic constrain
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Books on the topic "First Order Optimization Methods"

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Lan, Guanghui. First-order and Stochastic Optimization Methods for Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39568-1.

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G, Macaraeg Michelle, and Langley Research Center, eds. Preconditioning for first-order spectral discretizations. National Aeronautics and Space Administration, Langley Research Center, 1986.

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Subbotin, A. I. Generalized solutions of first-order PDEs: The dynamical optimization perspective. Birkhäuser, 1995.

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Pavarino, Luca F. Domain decomposition algorithms for first-order system least squares methods. Institute for Computer Applications in Science and Engineering, 1996.

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Pollock, John L. Technical methods in philosophy. Westview Press, 1990.

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David, Freed Alan, and Lewis Research Center, eds. Asymptotic integration algorithms for nonhomogeneous, nonlinear, first order, ordinary differential equations. Lewis Research Center, 1991.

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Haslach, Henry W. Nonlinear asymptotic integration algorithms for one-dimensional autonomous dissipative first-order ODEs. National Aeronautics and Space Administration, 1994.

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Fonseca, Carlos M. da. A panorama of mathematics: Pure and applied : Conference on Mathematics and Its Applications, November 14-17, 2014, Kuwait University, Safat, Kuwait. American Mathematical Society, 2016.

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Southeast Geometry Seminar (15th 2009 University of Alabama at Birmingham). Geometric analysis, mathematical relativity, and nonlinear partial differential equations: Southeast Geometry Seminars Emory University, Georgia Institute of Technology, University of Alabama, Birmingham, and the University of Tennessee, 2009-2011. Edited by Ghomi Mohammad 1969-. American Mathematical Society, 2013.

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Brodeckiy, Gennadiy, Denis Gusev, and Ivan Shidlovskiy. Multi-criteria choice in logistics research. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1902741.

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Methods and models of optimization of solutions under many criteria are considered, which are conditioned by modern tasks to improve the operation of supply chains and logistics systems. Attention is paid to their specifics in relation to the tasks of organizing transport supply support. The anomalous phenomena of "blindness" to the indicators of individual particular criteria and the phenomena of "blocking" the choice of alternatives when optimizing such systems are analyzed. Special modifications of traditional selection criteria for optimization are presented, allowing to eliminate these ph
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Book chapters on the topic "First Order Optimization Methods"

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Lan, Guanghui. "Nonconvex Optimization." In First-order and Stochastic Optimization Methods for Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39568-1_6.

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Lan, Guanghui. "Convex Optimization Theory." In First-order and Stochastic Optimization Methods for Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39568-1_2.

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Lan, Guanghui. "Deterministic Convex Optimization." In First-order and Stochastic Optimization Methods for Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39568-1_3.

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Lan, Guanghui. "Stochastic Convex Optimization." In First-order and Stochastic Optimization Methods for Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39568-1_4.

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Lan, Guanghui. "Projection-Free Methods." In First-order and Stochastic Optimization Methods for Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39568-1_7.

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Ferreira, Orizon P., Maurício S. Louzeiro, and Leandro F. Prudente. "First Order Methods for Optimization on Riemannian Manifolds." In Handbook of Variational Methods for Nonlinear Geometric Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31351-7_18.

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Liu, Shuai, and Claudia Sagastizábal. "Beyond First Order: V U $$\mathcal {V}\mathcal {U}$$ -Decomposition Methods." In Numerical Nonsmooth Optimization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34910-3_9.

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Lan, Guanghui. "Operator Sliding and Decentralized Optimization." In First-order and Stochastic Optimization Methods for Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39568-1_8.

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Bellavia, Stefania, Tommaso Bianconcini, Nataša Krejić, and Benedetta Morini. "Subsampled First-Order Optimization Methods with Applications in Imaging." In Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-98661-2_78.

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Bellavia, Stefania, Tommaso Bianconcini, Nataša Krejić, and Benedetta Morini. "Subsampled First-Order Optimization Methods with Applications in Imaging." In Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-03009-4_78-1.

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Conference papers on the topic "First Order Optimization Methods"

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Sharma, Ashutosh, Gauransh Dingwani, and Ishan Bajaj. "Interval Hessian-based Optimization Algorithm for Unconstrained Non-convex Problems." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.135173.

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Second-order optimization algorithms that leverage the exact Hessian or its approximation have been proven to achieve a faster convergence rate than first-order methods. However, their applications on training deep neural networks models, partial differential equations-based optimization problems, and large-scale non-convex problems, are hindered due to high computational cost associated with the Hessian evaluation, Hessian inversion to find the search direction, and ensuring its positive-definiteness. Accordingly, we propose a new search direction based on an interval Hessian and incorporate
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Hu, Bin, and Laurent Lessard. "Control interpretations for first-order optimization methods." In 2017 American Control Conference (ACC). IEEE, 2017. http://dx.doi.org/10.23919/acc.2017.7963426.

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Zhang, Xinwei, John Sartori, Mingyi Hong, and Sairaj Dhople. "DImplementing First-order Optimization Methods: Algorithmic Considerations and Bespoke Microcontrollers." In 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2019. http://dx.doi.org/10.1109/ieeeconf44664.2019.9048681.

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Devarakonda, Aditya, Kimon Fountoulakis, James Demmel, and Michael W. Mahoney. "Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization." In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2018. http://dx.doi.org/10.1109/ipdps.2018.00051.

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"First- and second-order aerodynamic sensitivity derivatives via automatic differentiation with incremental iterative methods." In 5th Symposium on Multidisciplinary Analysis and Optimization. American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-4262.

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Akin, M. Bugra, and Wolfgang Sanz. "A Quasi-First Order Optimization Method and its Demonstration on the Optimization of a U-Bend." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-42640.

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Optimal shape design is widely used today to improve a variety of designs. It is a challenging task and several methods have been developed. These methods are generally classified by the order of derivatives used. They are zero, first and second order methods, which, as their names imply, use only the function values, first and second order derivatives, respectively. There are two common approaches to first order methods. These are the finite difference method and the adjoint method. The finite difference method requires an additional CFD calculation for each parameter, which quickly becomes c
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Cao, Qiushi, and Prakash Krishnaswami. "In Support of Second Order Optimization." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0087.

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Abstract The vast majority of applied optimization falls into the category of first order optimization. This paper attempts to make the case for increased use of second order optimization techniques. Some of the most serious criticisms against second order methods are discussed and are shown to have lost some of their validity in recent years. In addition, some positive advantages of second order methods are also presented. These advantages include computational efficiency, compatibility with new advances in hardware and spill-over benefits in areas such as minimum sensitivity design. A simple
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FADEL, GEORGES, MICHAEL RILEY, and JEAN-FRANCOIS BARTHELEMY. "Improved First Order Approximation Method for Structural Optimization." In 31st Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics, 1990. http://dx.doi.org/10.2514/6.1990-1178.

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Lee, Bruce, and Peter Seiler. "Performance Analysis of First-order Optimization Methods Using Interpolation Conditions Without Function Evaluations." In 2021 American Control Conference (ACC). IEEE, 2021. http://dx.doi.org/10.23919/acc50511.2021.9483411.

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Taylor, Adrien B., Julien M. Hendrickx, and Francois Glineur. "Performance estimation toolbox (PESTO): Automated worst-case analysis of first-order optimization methods." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8263832.

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Reports on the topic "First Order Optimization Methods"

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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint
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Snopok, Pavel. Optimization of accelerator parameters using normal form methods on high-order transfer maps. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/924531.

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Smith, M. A., G. Palmiotti, and E. E. Lewis. Fuel cycle methods : first-order spherical harmonics formulations capable of treating low density regions. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/821070.

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Mitchell, Jason W. Implementing Families of Implicit Chebyshev Methods with Exact Coefficients for the Numerical Integration of First- and Second-Order Differential Equations. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada404958.

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Iyer, Ananth V., Samuel Labi, Steven R. Dunlop, et al. Heavy Fleet and Facilities Optimization. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317365.

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The Indiana Department of Transportation (INDOT) is responsible for timely clearance of snow on state-maintained highways in Indiana as part of its wintertime operations. For this and other maintenance purposes, the state’s subdistricts maintain 101 administrative units spread throughout the state. These units are staffed by personnel, including snow truck drivers and house snow removal trucks and other equipment. INDOT indicated a need to carry out value engineering analysis of the replacement timing of the truck fleet. To address these questions, this study carried out analysis to ascertain
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EFFC/DFI Support Fluids Task Group. EFFC/DFI Guide to Support Fluids for Deep Foundations , First Edition. European Federation of Foundation Contractors and Deep Foundations Institute, 2019. https://doi.org/10.37308/effc-dfi-sftg-guide-e1-2019.

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This guide represents the state of the art of support fluid practice and aims to improve existing design, testing and practices for deep foundation elements – bored piles (drilled shafts), barrettes (LBEs) and diaphragm wall panels. It represents the first time that knowledge of good practice from around the world has been brought together into a single authoritative publication. The purpose of this Guide is to present current understanding on bentonite, other clays, polymers and blended systems, including the advantages and limitations, in order to allow informed selection of the optimum tech
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Wang, Ziqi, and Jungho Kim. The 21st Working Conference of the IFIP Working Group 7.5 on Reliability and Optimization of Structural Systems (IFIP WG7.5 2024). Pacific Earthquake Engineering Research Center, 2024. https://doi.org/10.55461/vvll8567.

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These are the proceedings of the twenty-first working conference of the International Federation of Information Processing (IFIP) Working Group 7.5 on Reliability and Optimization of Structural Systems, which took place at the University of California, Berkeley, USA, on August 19–21, 2024. This volume contains 15 selected papers from the 20 presentations delivered at the conference. The conference was supported by Pacific Earthquake Engineering Research (PEER) Center, and by the University of California, Berkeley, which provided outstanding facilities in the conference venue and remarkable log
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Yang, Yu, Hen-Geul Yeh, and Bryan Aguirre. Fuel Cell System Development for Heavy Duty Vehicles. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2441.

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As California advances its ambitious goals for transportation electrification to combat climate change, hydrogen-powered fuel cells are emerging as a viable solution for overcoming the challenges of heavy-duty vehicles, offering an efficient alternative to lithium-ion batteries because they produce minimal chemical, thermal, and carbon emissions. One type of hydrogen fuel cell technology called proton exchange membrane fuel cells (PEMFCs) has garnered the most attention due to its distinct advantages, including relatively low operating temperatures (60–80 °C) and reliable performance at high c
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Oliver, Sandy, Dayana Minchenko, Mukdarut Bangpan, Kelly Dickson, Claire Stansfield, and Janice Tripney. Evidence claims for informing decisions relating to socio-economic development. Centre for Excellence and Development Impact and Learning (CEDIL), 2023. http://dx.doi.org/10.51744/llp2.

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The Centre of Excellence for Development Impact and Learning (CEDIL) develops and tests innovative methods for evaluation and evidence synthesis. Claims made in CEDIL studies are intended to inform socio-economic development in low- and middle-income countries (LMICs), or research about LMICs. This paper provides an overview of how, in CEDIL-funded studies, claims arising from research (termed ‘evidence claims’ for brevity) have been justified and communicated in order to inform policy decisions relating to socio-economic development. This study addresses two important questions about research
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Alexander, Chris. PR652-184505-R01 Evaluating Installation Techniques for Pipeline Repair Methods. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0012029.

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Abstract:
A testing program was conducted to evaluate the effects of internal pressure during the installation of composite and steel sleeves repair systems on pipelines with plain dents. The testing program included cyclic pressure testing a group of 12.75-inch OD x 0.188-inch WT, Grade X42 pipe samples with plain dents having residual dent depths on the order of 3% to 4% of the pipe's outside diameter. The dent samples were repaired using four (4) different composite repair systems, type-A steel sleeves, and steel thermal compression sleeves. The composite repair systems included a carbon fiber wet-la
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