Relevant bibliographies by topics / Parallel Interior Point Solver

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

Academic literature on the topic 'Parallel Interior Point Solver'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Parallel Interior Point Solver"

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Gondzio, Jacek, and Robert Sarkissian. "Parallel interior-point solver for structured linear programs." Mathematical Programming 96, no. 3 (2003): 561–84. http://dx.doi.org/10.1007/s10107-003-0379-5.

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Gondzio, Jacek, and Andreas Grothey. "Parallel interior-point solver for structured quadratic programs: Application to financial planning problems." Annals of Operations Research 152, no. 1 (2006): 319–39. http://dx.doi.org/10.1007/s10479-006-0139-z.

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Rehfeldt, Daniel, Hannes Hobbie, David Schönheit, Thorsten Koch, Dominik Möst, and Ambros Gleixner. "A massively parallel interior-point solver for LPs with generalized arrowhead structure, and applications to energy system models." European Journal of Operational Research 296, no. 1 (2022): 60–71. http://dx.doi.org/10.1016/j.ejor.2021.06.063.

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Xu, Kun, and George A. McMechan. "3D scalar-wave absorbing boundary conditions with optimal coefficients in the frequency-space domain." GEOPHYSICS 77, no. 3 (2012): T83—T96. http://dx.doi.org/10.1190/geo2011-0412.1.

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To improve the computational efficiency for the solution of the 3D Helmholtz equation in the frequency-space domain, high-order compact forms of finite differences are preferred. We applied a pointwise Padé approximation to develop a 3D 27-point fourth-order compact finite-difference (FD) stencil in the grid interior, with a space-differentiated source term, for the scalar-wave equation; this has similar high-accuracy (4–5 grid points per the shortest wavelength) to another 27-point fourth-order FD stencil using a parsimonious mixed-grid and staggered-grid combination, but is much simpler. For absorbing boundary conditions (ABCs), a damping zone is expensive, and a perfectly matched layer can not be straightforwardly introduced into the compact FD form for the second-order wave equation. Thus, we developed 3D one-way wave equation (OWWE) ABCs with adjustable coefficients. They have different angle approximations and FD forms for the six faces, twelve edges, and eight corners in 3D models to fit with the interior compact FD form. By adjusting the coefficients to the optimum, the OWWE ABCs have wider-angle absorbing ability than those without optimal coefficients. Finally, all the interior and boundary FD forms were combined into a sparse complex-valued impedance matrix of the frequency-space modeling equation, and solved for each frequency. Because the storage of the sparse impedance matrix was determined by the 3D discrete grid size, the OWWE ABCs with only one outer layer needed the minimum grid size compared with other ABCs, thus were the most efficient for the solution of the impedance matrix. The modeling algorithm was performed on multicore processors using a MPI parallel direct solver. Numerical tests on homogeneous and heterogeneous models gave satisfactory absorbing effects.
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Czyzyk, Joseph, Robert Fourer, and Sanjay Mehrotra. "Using a Massively Parallel Processor to Solve Large Sparse Linear Programs by an Interior-Point Method." SIAM Journal on Scientific Computing 19, no. 2 (1998): 553–65. http://dx.doi.org/10.1137/s1064827594272086.

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Luo, Zhijun, and Lirong Wang. "A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization." Journal of Mathematics 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/461902.

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A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.
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Li, Nan, Shou Zhen Zhu, Jing Hong Zheng, and Xiao Min Bai. "Capacity Limit of Grid-Connected Wind/Photovoltaic Hybrid Power System Based on Interior Point Algorithm." Applied Mechanics and Materials 448-453 (October 2013): 2461–67. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.2461.

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Wind/Photovoltaic hybrid generating system can alleviate power fluctuations caused by the single source and improve the system power supply reliability. In the stage of planning, the grid-connected scheme and capacity limit calculation problem is needed to solve. Under different schemes, the capacity limit is different. This paper established an optimization model of capacity limit of wind/photovoltaic power station, introduced the tracking center trajectory interior point algorithm to solve capacity limit, and demonstrated the validity of the proposed method by a simulation of IEEE 30-node system considering the reserve capacity, and concluded that capacity limit is larger when Wind/Photovoltaic is parallel in heavy load nodes. This method can provide reference for practical system of calculating capacity limit of wind farm or photovoltaic power station and is feasible.
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Costantini, Giuliano, and Daniel Görges. "Fast distributed model predictive control combining ADMM, IPM and Riccati iteration." at - Automatisierungstechnik 69, no. 2 (2021): 97–110. http://dx.doi.org/10.1515/auto-2020-0106.

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Abstract In model predictive control, the control action is found at each sampling time by solving an online optimization problem. Computationally, this step is very demanding, especially if compared to the evaluation of traditional control laws. This has limited the application of model predictive control to systems with slow dynamics for many years. Recently, several methods have been proposed in the literature which promise a substantial reduction of the computation time by either running the computation in parallel (distributed model predictive control) or exploiting the problem structure (fast model predictive control). A combination of these methods has not yet been considered in the literature. To achieve this goal, different optimization techniques need to be employed at once. The order of how these methods are applied matters. This paper considers fast distributed model predictive control combining the alternating direction method of multipliers (ADMM), the interior point method (IPM) and the Riccati iteration for a particular class of multi-agent systems for which the order of the methods can be arbitrarily changed. This leads to two different solver schemes where a trade-off arises between computation time and number of communications required to reach consensus. A simplified problem involving the formation control of a fleet of vehicles is considered at the end.
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Jarron, D., M. Shahbazi, D. Lichti, and R. Radovanovic. "AUTOMATIC DETECTION AND LABELLING OF PHOTOGRAMMETRIC CONTROL POINTS IN A CALIBRATION TEST FIELD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1673–80. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1673-2019.

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<p><strong>Abstract.</strong> In this work, a new method is developed for the automatic and accurate detection and labelling of signalized, un-coded circular targets for the purpose of automated camera calibration in a test field. The only requirements of this method are the approximate height of the camera, an approximate range of orientations of the camera, and the object-space coordinates of the targets. In each image, circular targets are detected using adaptive thresholding and robust ellipse fitting. Labelling of those targets is performed next. First, the exterior orientation parameters of the image are estimated using a one-point pose-estimation approach, where a list of possible orientation and target labels are used, along with height, to calculate the camera position. The estimated position and orientation of the camera combined with the interior orientation parameters (IOPs) are then used to back-project the known object-space coordinates of the targets into the image space. These targets are then matched against the targets detected in the image, and the list entry with the best fit is chosen as the solution. This resolves both the detection and labelling of the targets, without the need for any coded targets or their associated software packages, and each image is solved independently allowing for parallel processing. This process accurately labels 92–97% of images, with average accuracy rates of 97% or better, and average completeness rates of 70–95% in imagery from the three cameras tested. The cameras were calibrated using observations from the detection and labelling process, which resulted in sub-pixel root mean square (RMS) values determined for the pixel space residuals.</p>
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Castro, Jordi. "Interior-point solver for convex separable block-angular problems." Optimization Methods and Software 31, no. 1 (2015): 88–109. http://dx.doi.org/10.1080/10556788.2015.1050014.

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Dissertations / Theses on the topic "Parallel Interior Point Solver"

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Oliveira, Bráulio César de. "Estimação de estados em sistemas de distribuição: uma abordadgem trifásica e descentralizada." Universidade Federal de Juiz de Fora (UFJF), 2016. https://repositorio.ufjf.br/jspui/handle/ufjf/3141.

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Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-01-09T11:36:05Z No. of bitstreams: 1 brauliocesardeoliveira.pdf: 2150243 bytes, checksum: 62faa254539b7873aa1393d8cd8f1bf2 (MD5)<br>Approved for entry into archive by Diamantino Mayra (mayra.diamantino@ufjf.edu.br) on 2017-01-31T11:23:24Z (GMT) No. of bitstreams: 1 brauliocesardeoliveira.pdf: 2150243 bytes, checksum: 62faa254539b7873aa1393d8cd8f1bf2 (MD5)<br>Made available in DSpace on 2017-01-31T11:23:24Z (GMT). No. of bitstreams: 1 brauliocesardeoliveira.pdf: 2150243 bytes, checksum: 62faa254539b7873aa1393d8cd8f1bf2 (MD5) Previous issue date: 2016-03-08<br>O presente trabalho tem por objetivo apresentar uma metodologia para estimação de estados em sistemas de distribuição de energia elétrica. São utilizadas como variáveis de estado as correntes nos ramos. As medições são obtidas por meio de medições fasoriais sincronizadas(PhasorMeasurementUnits-PMUs),sendoqueostiposdemedidasadvindos desses equipamentos são as tensões nodais e as correntes nos ramos. A abordagem é trifásica, portanto representa as características próprias de um sistema de distribuição. A metodologia consiste em resolver um problema de otimização não linear cuja função objetivo associa o erro quadrático das medidas em relação aos estados estimados sujeito às restrições de carga das barras da rede que não possuem PMUs instaladas baseadas em estimativas de cargas obtidas para o instante “t-1”, partindo-se da premissa que em curtos intervalos de tempo a carga não sofre grandes variações, sendo esta em conjunto com a abordagem trifásica as principais contribuições deste trabalho. Outra contribuição do trabalho é a descentralização, com esta técnica pode-se dividir uma determinada rede em vários subsistemas que podem ser resolvidos de forma separada e independente. Isso torna o processo mais rápido do ponto de vista computacional além de permitir o uso do processamento paralelo, visto que já existe um paralelismo natural entre as tarefas que devem ser resolvidas. Outra vantagem da divisão em subsistemas reside no fato do monitoramento de áreas de interesse. Para utilizar a descentralização foi proposta uma alternativa de alocação de PMUs que consiste em posicionar duas unidades em cada ramificação do sistema, uma no começo e outra no final do trecho, procurando utilizar o menor número possível e que não comprometa a qualidade dos estados estimados. A resolução do problema de otimização é realizada através da implementação computacional do Método de Pontos Interiores com Barreira de Segurança (Safety Barrier Interior Point Method - SFTB - IPM) proposto na literatura especializada. As medidas das PMUs foram obtidas através de um Fluxo de Potência Trifásico via Injeção de Correntes (FPTIC). Foram realizadas diversas simulações variando-se o percentual da carga e os resultados obtidos foram comparados com outra metodologia existente na literatura e com os valores verdadeiros que foram obtidos através do FPTIC para as barras não monitoradas. Foram tambémcomparadosotempocomputacionalentreaexecuçãoserialeaexecuçãoutilizando o processamento paralelo. Os testes mostraram bons resultados o que torna a metodologia proposta aplicável na supervisão de sistemas de distribuição.<br>This work aims to present a methodology for static state estimation in electric power distribution systems. Branch currents are used as state variables. Measurements are obtained by means of Phasor Measurement Units (PMUs), in which voltage and current branches measurements are used. The approach is three-phase, thus represents the distribution system characteristics. The methodology consists of solving a nonlinear optimization problem minimizing a quadratic objective function associated with the estimated measurements and states subject to load constraints for the non monitored loads based on estimated load obtained from the ‘t-1’ instant, starting from the assumption that in short time intervals the load does not have large variations, which together with the the three-phase approach are the main contributions of this work. Another contribution of this work is the descentralided approach, with this assumption the network can be divided into several subnetworks that can be solved separately and independently. This speeds up the process of being solved from a computational point of view and allows the use of parallel processing, since there is already a natural parallelism among tasks to be solved. Another advantage of the division into subsystems is the fact that the monitoring areas of interest. With the aim of allowing the decentralization was proposed PMUs allocation strategy that consists of allocating two units for each lateral feeder, one at the beginning and one at the end, trying to use as little PMUs as possible in such a way that the quality of the estimated states are not compromised. The resolution of the optimization problem is done through a computer implementation of Interior Point Method with Security Barrier (SFTB - IPM) proposed in the literature. The PMUs measurements were emulated using a Three-PhasePowerFlowusingtheCurrentInjectionmethod(FPTIC).Severalsimulations were performed varying the load percentage and the results obtained were compared with other existing methodology in literature and also the true values that were obtained from the FPTIC to non monitored loads. The computational time using serial and parallel processing were also compared. Results show good results which makes the proposed methodology applicable in monitoring distribution systems.
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Wong, David C. "Advanced Interior Point Formulation for the Global Routing Problem." Thesis, 2009. http://hdl.handle.net/10012/4397.

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As the circuit size increases in modern electronics, the design process becomes more complicated. Even though the hardware design process is divided into multiple phases, many of the divided problems are still extremely time consuming to solve. One of these NP-hard problems is the routing problem. As electronics step into the deep submicron era, optimizing the routing becomes increasingly important. One of the methods to solve global routing is to formulate the problem as an integer programming (IP) problem. This formulation can then be relaxed into a linear programming problem and solved using interior point method. This thesis investigates two new approaches to optimize the speed of solving global routing using Karmarkar’s interior point method, as well as the effect of combining various optimizations with these new approaches. The first proposed approach is to utilize solution stability as the interior point loop converges, and attempt to remove solutions that have already stabilized. This approach reduces the problem size and allows subsequent interior point iterations to proceed faster. The second proposed approach is to solve the inner linear system (projection step) in interior point method in parallel. Experimental results show that for large routing problems, the performance of the solver is improved by the optimization approaches. The problem reduction stage allows for great speedup in the interior point iterations, without affecting the quality of the solution significantly. Furthermore, the timing required to solve inner linear system in the interior point method is improved by solving the problem in parallel. With these optimizations, solving the routing problem using the IP formation becomes increasingly more efficient. By solving an efficient parallel IP formation rather than a traditional sequential approach, more efficient optimal solutions which incorporate multiple conflicting objectives can be achieved.
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Bardhan, Jaydeep P., J. H. Lee, Shihhsien Kuo, Michael D. Altman, Bruce Tidor, and Jacob K. White. "Fast Methods for Bimolecular Charge Optimization." 2003. http://hdl.handle.net/1721.1/3711.

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We report a Hessian-implicit optimization method to quickly solve the charge optimization problem over protein molecules: given a ligand and its complex with a receptor, determine the ligand charge distribution that minimizes the electrostatic free energy of binding. The new optimization couples boundary element method (BEM) and primal-dual interior point method (PDIPM); initial results suggest that the method scales much better than the previous methods. The quadratic objective function is the electrostatic free energy of binding where the Hessian matrix serves as an operator that maps the charge to the potential. The unknowns are the charge values at the charge points, and they are limited by equality and inequality constraints that model physical considerations, i.e. conservation of charge. In the previous approaches, finite-difference method is used to model the Hessian matrix, which requires significant computational effort to remove grid-based inaccuracies. In the novel approach, BEM is used instead, with precorrected FFT (pFFT) acceleration to compute the potential induced by the charges. This part will be explained in detail by Shihhsien Kuo in another talk. Even though the Hessian matrix can be calculated an order faster than the previous approaches, still it is quite expensive to find it explicitly. Instead, the KKT condition is solved by a PDIPM, and a Krylov based iterative solver is used to find the Newton direction at each step. Hence, only Hessian times a vector is necessary, which can be evaluated quickly using pFFT. The new method with proper preconditioning solves a 500 variable problem nearly 10 times faster than the techniques that must find a Hessian matrix explicitly. Furthermore, the algorithm scales nicely due to the robustness in number of IPM iterations to the size of the problem. The significant reduction in cost allows the analysis of much larger molecular system than those could be solved in a reasonable time using the previous methods.<br>Singapore-MIT Alliance (SMA)
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Talavatifard, Habiballah. "Application of L1 Minimization Technique to Image Super-Resolution and Surface Reconstruction." Thesis, 2013. http://hdl.handle.net/1969.1/149512.

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A surface reconstruction and image enhancement non-linear finite element technique based on minimization of L1 norm of the total variation of the gradient is introduced. Since minimization in the L1 norm is computationally expensive, we seek to improve the performance of this algorithm in two fronts: first, local L1- minimization, which allows parallel implementation; second, application of the Augmented Lagrangian method to solve the minimization problem. We show that local solution of the minimization problem is feasible. Furthermore, the Augmented Lagrangian method can successfully be used to solve the L1 minimization problem. This result is expected to be useful for improving algorithms computing digital elevation maps for natural and urban terrain, fitting surfaces to point-cloud data, and image super-resolution.
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Books on the topic "Parallel Interior Point Solver"

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J, Andersen, ed. Adapting the interior point method for the solution of LPs on serial, coarse grain parallel and massive parallel computers. Brunel University, Department of Mathematics and Statistics, 1990.

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Book chapters on the topic "Parallel Interior Point Solver"

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Kardoš, Juraj, Drosos Kourounis, and Olaf Schenk. "Structure-Exploiting Interior Point Methods." In Parallel Algorithms in Computational Science and Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43736-7_3.

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Berthold, Timo, Michael Perregaard, and Csaba Mészáros. "Four Good Reasons to Use an Interior Point Solver Within a MIP Solver." In Operations Research Proceedings. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89920-6_22.

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Smith, Edmund, Jacek Gondzio, and Julian Hall. "GPU Acceleration of the Matrix-Free Interior Point Method." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31464-3_69.

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Levkovitz, R., J. Andersen, and G. Mitra. "The interior point method for LP on parallel computers." In System Modelling and Optimization. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/bfb0113291.

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Karypius, George, Anshul Gupta, and Vipin Kumar. "Experiences with a parallel formulation of an interior point algorithm." In Parallel Processing of Discrete Optimization Problems. American Mathematical Society, 1995. http://dx.doi.org/10.1090/dimacs/022/07.

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Dennis, J. E., and Luís N. Vicente. "Trust-Region Interior-Point Algorithms for Minimization Problems with Simple Bounds." In Applied Mathematics and Parallel Computing. Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-99789-1_7.

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Mészáros, Csaba. "The Bpmpd Interior Point Solver for Convex Quadratically Constrained Quadratic Programming Problems." In Large-Scale Scientific Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12535-5_97.

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Mangoni, Dario, Alessandro Tasora, and Simone Benatti. "Interior-Point Solver for Non-smooth Multi-Body Dynamics with Finite Elements." In Multibody Dynamics 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23132-3_41.

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Potra, Florian A. "Implementation of Interior Point Methods on Parallel and Vector Machines." In Operations Research ’91. Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-642-48417-9_52.

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Castro, Jordi, and Antonio Frangioni. "A Parallel Implementation of an Interior-Point Algorithm for Multicommodity Network Flows." In Vector and Parallel Processing — VECPAR 2000. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44942-6_25.

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Conference papers on the topic "Parallel Interior Point Solver"

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Goldberg, A. V., D. B. Shmoys, S. A. Plotkin, and E. Tardos. "Interior-point methods in parallel computation." In 30th Annual Symposium on Foundations of Computer Science. IEEE, 1989. http://dx.doi.org/10.1109/sfcs.1989.63502.

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Karypis, George, Anshul Gupta, and Vipin Kumar. "A parallel formulation of interior point algorithms." In the 1994 ACM/IEEE conference. ACM Press, 1994. http://dx.doi.org/10.1145/602770.602808.

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BERGAMASCHI, LUCA, and GIOVANNI ZILLI. "PARALLEL INEXACT NEWTON AND INTERIOR POINT METHODS." In Proceedings of the International Conference ParCo99. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2000. http://dx.doi.org/10.1142/9781848160170_0090.

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Zhang, Xi, Laura Ferranti, and Tamas Keviczky. "An improved primal-dual interior point solver for model predictive control." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8263807.

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Jerez, Juan L., George A. Constantinides, and Eric C. Kerrigan. "FPGA implementation of an interior point solver for linear model predictive control." In 2010 International Conference on Field-Programmable Technology (FPT). IEEE, 2010. http://dx.doi.org/10.1109/fpt.2010.5681439.

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Liu, Bo, Lawryn Edmonds, Hang Zhang, and Hongyu Wu. "An Interior-Point Solver for Optimal Power Flow Problem Considering Distributed FACTS Devices." In 2020 IEEE Kansas Power and Energy Conference (KPEC). IEEE, 2020. http://dx.doi.org/10.1109/kpec47870.2020.9167620.

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Ghami, Mohamed El, Ivan Ivanov, and Trond Steihaug. "Primal-dual interior-point methods solver based on kernel functions for Linear Optimization." In 2009 International Multiconference on Computer Science and Information Technology (IMCSIT). IEEE, 2009. http://dx.doi.org/10.1109/imcsit.2009.5352756.

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Linfeng, Yang, Li Taoshen, and Li Jie. "Parallel Predictor-Corrector Interior-Point Algorithm of Structured Optimization Problems." In 2009 3rd International Conference on Genetic and Evolutionary Computing (WGEC). IEEE, 2009. http://dx.doi.org/10.1109/wgec.2009.68.

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Yang, Linfeng, Jinbao Jian, and Taoshen Li. "Parallel Interior-Point Algorithm Based on MCWC for ESC-OPF." In 2009 International Conference on Information Technology and Computer Science (ITCS 2009). IEEE, 2009. http://dx.doi.org/10.1109/itcs.2009.190.

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Jin, Jing, Xianggao Cai, and Xiaola Lin. "Efficient SVM Training Using Parallel Primal-Dual Interior Point Method on GPU." In 2013 International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE, 2013. http://dx.doi.org/10.1109/pdcat.2013.9.

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