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Academic literature on the topic 'Generalized trust region subproblem'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Generalized trust region subproblem"

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Pong, Ting Kei, and Henry Wolkowicz. "The generalized trust region subproblem." Computational Optimization and Applications 58, no. 2 (January 17, 2014): 273–322. http://dx.doi.org/10.1007/s10589-013-9635-7.

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Jia, Zhongxiao, and Fa Wang. "The Convergence of the Generalized Lanczos Trust-Region Method for the Trust-Region Subproblem." SIAM Journal on Optimization 31, no. 1 (January 2021): 887–914. http://dx.doi.org/10.1137/19m1279691.

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Adachi, Satoru, Satoru Iwata, Yuji Nakatsukasa, and Akiko Takeda. "Solving the Trust-Region Subproblem By a Generalized Eigenvalue Problem." SIAM Journal on Optimization 27, no. 1 (January 2017): 269–91. http://dx.doi.org/10.1137/16m1058200.

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Salahi, M., and A. Taati. "An efficient algorithm for solving the generalized trust region subproblem." Computational and Applied Mathematics 37, no. 1 (May 13, 2016): 395–413. http://dx.doi.org/10.1007/s40314-016-0349-1.

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Jiang, Rujun, and Duan Li. "Novel Reformulations and Efficient Algorithms for the Generalized Trust Region Subproblem." SIAM Journal on Optimization 29, no. 2 (January 2019): 1603–33. http://dx.doi.org/10.1137/18m1174313.

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Wang, Shu, and Yong Xia. "Strong duality for generalized trust region subproblem: S-lemma with interval bounds." Optimization Letters 9, no. 6 (October 14, 2014): 1063–73. http://dx.doi.org/10.1007/s11590-014-0812-0.

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Zhou, Jing, Cheng Lu, Ye Tian, and Xiaoying Tang. "A SOCP relaxation based branch-and-bound method for generalized trust-region subproblem." Journal of Industrial & Management Optimization 17, no. 1 (2021): 151–68. http://dx.doi.org/10.3934/jimo.2019104.

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Jin, Qingwei, Shu-Cherng Fang, and Wenxun Xing. "On the global optimality of generalized trust region subproblems." Optimization 59, no. 8 (November 2010): 1139–51. http://dx.doi.org/10.1080/02331930902995236.

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Jiang, Rujun, and Duan Li. "A Linear-Time Algorithm for Generalized Trust Region Subproblems." SIAM Journal on Optimization 30, no. 1 (January 2020): 915–32. http://dx.doi.org/10.1137/18m1215165.

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Jiang, Rujun, Duan Li, and Baiyi Wu. "SOCP reformulation for the generalized trust region subproblem via a canonical form of two symmetric matrices." Mathematical Programming 169, no. 2 (April 18, 2017): 531–63. http://dx.doi.org/10.1007/s10107-017-1145-4.

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Dissertations / Theses on the topic "Generalized trust region subproblem"

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Grodzevich, Oleg. "Regularization Using a Parameterized Trust Region Subproblem." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/1159.

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We present a new method for regularization of ill-conditioned problems that extends the traditional trust-region approach. Ill-conditioned problems arise, for example, in image restoration or mathematical processing of medical data, and involve matrices that are very ill-conditioned. The method makes use of the L-curve and L-curve maximum curvature criterion as a strategy recently proposed to find a good regularization parameter. We describe the method and show its application to an image restoration problem. We also provide a MATLAB code for the algorithm. Finally, a comparison to the CGLS approach is given and analyzed, and future research directions are proposed.
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Fortin, Charles. "A survey of the trust region subproblem within a semidefinite framework." Thesis, University of Waterloo, 2000. http://hdl.handle.net/10012/1038.

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Trust region subproblems arise within a class of unconstrained methods called trust region methods. The subproblems consist of minimizing a quadratic function subject to a norm constraint. This thesis is a survey of different methods developed to find an approximate solution to the subproblem. We study the well-known method of More and Sorensen and two recent methods for large sparse subproblems: the so-called Lanczos method of Gould et al. and the Rendland Wolkowicz algorithm. The common ground to explore these methods will be semidefinite programming. This approach has been used by Rendl and Wolkowicz to explain their method and the More and Sorensen algorithm; we extend this work to the Lanczos method. The last chapter of this thesis is dedicated to some improvements done to the Rendl and Wolkowicz algorithm and to comparisons between the Lanczos method and the Rendl and Wolkowicz algorithm. In particular, we show some weakness of the Lanczos method and show that the Rendl and Wolkowicz algorithm is more robust.
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Yang, Boshi. "A conic optimization approach to variants of the trust region subproblem." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1938.

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The Trust Region Subproblem (TRS), which minimizes a nonconvex quadratic function over the unit ball, is an important subproblem in trust region methods for nonlinear optimization. Even though TRS is a nonconvex problem, it can be solved in polynomial time using, for example, a semidefinite programming (SDP) relaxation. Different variants of TRS have been considered from both theoretical and practical perspectives. In this thesis, we study three variants of TRS and their SDP/conic relaxations. We first study an extended trust region subproblem (eTRS) in which the trust region equals the intersection of the unit ball with M linear cuts. When m = 0, when m = 1, or when m = 2 and the linear cuts are parallel, it is known that the eTRS optimal value equals the optimal value of a particular conic relaxation, which is solvable in polynomial time. However, it is also known that, when m ≥2 and at least two of the linear cuts intersect within the ball, i.e., some feasible point of the eTRS satisfies both linear constraints at equality, then the same conic relaxation may admit a gap with eTRS. We show that the conic relaxation admits no gap for arbitrary M as long as the linear cuts are non-intersecting. We then extend our result to a more general setting. We study an eTRS in which a quadratic function is minimized over a structured nonconvex feasible region: the unit ball with M linear cuts and R hollows. In the special case when m = 0 and r = 1, it is known that the eTRS has a tight polynomial-time solvable conic relaxation. We show that a certain conic relaxation is also tight for general R and M as long as the cuts and hollows satisfy some non-intersecting assumptions that generalize the previous paragraph. Finally, intersecting the feasible region of TRS with a second ellipsoid results in the two-trust-region subproblem (TTRS). Even though TTRS can also be solved in polynomial-time, existing approaches do not provide a concise conic relaxation. We investigate the use of conic relaxation for TTRS. Starting from the basic SDP relaxation of TTRS, which admits a gap, recent research has tightened the basic relaxation using valid second-order-cone (SOC) inequalities. For the special case of TTRS in dimension n=2, we fully characterize the remaining valid inequalities, which can be viewed as strengthened versions of the SOC inequalities just mentioned. We also demonstrate that these valid inequalities can be used computationally even when n > 2 to solve TTRS instances that were previously unsolved using techniques of conic relaxation.
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Fortin, Charles. "Computing the local minimizers of a large and sparse trust-region subproblem." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85548.

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We present new algorithms for computing local minimizers of the trust-region subproblem (TRS). This problem consists in minimizing a quadratic function subject to a ball constraint. In particular, this problem appears as a subproblem in trust-region methods for constrained and unconstrained optimization. First, by modeling the TRS with a new semidefinite program, different than the standard semidefinite relaxation, we derive an algorithm, similar in structure to the Rendl-Wolkowicz Algorithm, which implicitly solves the semidefinite program by maximizing a single variable concave function over a closed interval. Second, we extend the theory needed for this algorithm and the Rendl-Wolkowicz Algorithm to derive two algorithms for computing a local-nonglobal minimizer of the TRS. These algorithms are based upon finding a root of a single variable convex function with the secant method. In all our algorithms, we compute at most the two smallest eigenvalues of a parameterized matrix. This can be done using an ARPACK subroutine which only requires matrix-vector multiplications. Hence, we are able to exploit the possible sparsity of the Hessian matrix of the quadratic objective, making the algorithms suitable for large problems. Computationally, the algorithms for finding a local-nonglobal minimizer are more competitive than the previous approach based on computing a matrix factorization at each iteration.
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Bralic, Nikola. "Ethnic identity and generalized trust in heterogenous environments : A comparative study in the Gothenburg region." Thesis, Högskolan Väst, Avd för juridik, ekonomi, statistik och politik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-4432.

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This thesis has studied two things, the first thing is to see if contact between different ethnic minorities but were the ethnic majority is absent has the same alleged positive effect on generalized trust as contact between ethnic minorities and the ethnic majority. The second issue is to see if people that have contact with people with other ethnic origin than themselves changed their perception of their ethnic identity and if that effects generalized trust. This was done by using theories of social constructivism and social capital were I had an inductive theoretical approach. The research design was comparative research design, comparing heterogeneous environments were there were different ethnic minorities but were the ethnic majority was absent to ethnic heterogeneous environments were there were different ethnic minorities but were the ethnic majority was present using semis structured interviews. What I found out is that contact did in deed effect how people perceived their ethnic identity but not always in the way that the theories suggested that they should. Age seemed to have an bigger effect on generalized trust then ethnic heterogeneity. Also ethnic identity did not directly effect generalized trust but that contact and ethnic identity did in some indirect way effect generalized trust because contact led people to believe that all people are the same no matter their ethnic identity.
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Lu, Zhaosong. "Algorithm Design and Analysis for Large-Scale Semidefinite Programming and Nonlinear Programming." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7151.

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The limiting behavior of weighted paths associated with the semidefinite program (SDP) map $X^{1/2}SX^{1/2}$ was studied and some applications to error bound analysis and superlinear convergence of a class of primal-dual interior-point methods were provided. A new approach for solving large-scale well-structured sparse SDPs via a saddle point mirror-prox algorithm with ${cal O}(epsilon^{-1})$ efficiency was developed based on exploiting sparsity structure and reformulating SDPs into smooth convex-concave saddle point problems. An iterative solver-based long-step primal-dual infeasible path-following algorithm for convex quadratic programming (CQP) was developed. The search directions of this algorithm were computed by means of a preconditioned iterative linear solver. A uniform bound, depending only on the CQP data, on the number of iterations performed by a preconditioned iterative linear solver was established. A polynomial bound on the number of iterations of this algorithm was also obtained. One efficient ``nearly exact' type of method for solving large-scale ``low-rank' trust region subproblems was proposed by completely avoiding the computations of Cholesky or partial Cholesky factorizations. A computational study of this method was also provided by applying it to solve some large-scale nonlinear programming problems.
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Ye, Heng. "Efficient Trust Region Subproblem Algorithms." Thesis, 2011. http://hdl.handle.net/10012/6297.

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The Trust Region Subproblem (TRS) is the problem of minimizing a quadratic (possibly non-convex) function over a sphere. It is the main step of the trust region method for unconstrained optimization problems. Two cases may cause numerical difficulties in solving the TRS, i.e., (i) the so-called hard case and (ii) having a large trust region radius. In this thesis we give the optimality characteristics of the TRS and review the major current algorithms. Then we introduce some techniques to solve the TRS efficiently for the two difficult cases. A shift and deflation technique avoids the hard case; and a scaling can adjust the value of the trust region radius. In addition, we illustrate other improvements for the TRS algorithm, including: rotation, approximate eigenvalue calculations, and inverse polynomial interpolation. We also introduce a warm start approach and include a new treatment for the hard case for the trust region method. Sensitivity analysis is provided to show that the optimal objective value for the TRS is stable with respect to the trust region radius in both the easy and hard cases. Finally, numerical experiments are provided to show the performance of all the improvements.
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Wang, Zhen. "A generalized trust region SQP algorithm for equality constrained optimization." Thesis, 2004. http://hdl.handle.net/1911/18720.

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We introduce and analyze a class of generalized trust region sequential quadratic programming (GTRSQP) algorithms for equality constrained optimization. Unlike in standard trust region SQP (TRSQP) algorithms, the optimization subproblems arising in our GTRSQP algorithm can be generated from models of the objective and constraint functions that are not necessarily based on Taylor approximations. The need for such generalizations is motivated by optimal control problems for which model problems can be generated using, e.g., different discretizations. Several existing TRSQP algorithms are special cases of our GTRSQP algorithm. Our first order global convergence result for the GTRSQP algorithm applied to TRSQP allows one to relax the condition that the so-called tangential step lies in the null-space of the linearized constraints. The application of the GTRSQP algorithm to an optimal control problem governed by Burgers equation is discussed.
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Chen, Yen-Ting, and 陳彥廷. "Generalized Reduced Trust-region Search and Its Applications to Statistical Multi-Objective Optimization." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/15245552797594517046.

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碩士
國立臺灣大學
工業工程學研究所
96
“Generalized Reduced Gradient” method is a popular NLP method, but it often incurs a zigzagging search path especially for the statistical multi-objective optimization (SMOO) problem where the objective function is a quartic function. In this study, we improve the “Trust Region (TR)” search method and develop the “Generalized Reduced Trust Region” (GRT) search method which combines the GRG method and the improved TR method. The GRT search transforms the constrained NLP problem to an unconstrained NLP problem consisting of only the nonbasic variables and searches the best improving direction in the reduced space. The proposed method is shown to overcome the zigzagging problem of the GRG method. To verify the performance of our methods, we study a well know test problem and three cases. The test problem is called Rosenbrock’s function which has a quartic objective function with two decision variables. The first case is a semiconductor design for manufacturing (DFM) problem. The second case is the problem to configure a robust semiconductor supply chain. The final case is the “Track System PEB CDU Optimization”. Compared against the result of the commercial software “Lingo”, the same or better solutions are obtained by our methods with comparable computation time.
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Chen, Yen-Ting. "Generalized Reduced Trust-region Search and Its Applications to Statistical Multi-Objective Optimization." 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2708200816423300.

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Book chapters on the topic "Generalized trust region subproblem"

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Jiang, Rujun, and Duan Li. "On Conic Relaxations of Generalization of the Extended Trust Region Subproblem." In Advances in Intelligent Systems and Computing, 145–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21803-4_15.

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Absil, P. A., C. G. Baker, K. A. Gallivan, and A. Sameh. "Adaptive Model Trust Region Methods for Generalized Eigenvalue Problems." In Lecture Notes in Computer Science, 33–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428831_5.

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Baker, C. G., P. A. Absil, and K. A. Gallivan. "An Implicit Riemannian Trust-Region Method for the Symmetric Generalized Eigenproblem." In Computational Science – ICCS 2006, 210–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758501_32.

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"7. The Trust-Region Subproblem." In Trust Region Methods, 169–248. Society for Industrial and Applied Mathematics, 2000. http://dx.doi.org/10.1137/1.9780898719857.ch7.

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Bezena, Ivan. "MODERN MECHANISMS OF ANTI-CRISIS REGIONAL MANAGEMENT OF EDUCATION IN THE CONDITIONS OF REFORM." In Development of scientific, technological and innovation space in Ukraine and EU countries. Publishing House “Baltija Publishing”, 2021. http://dx.doi.org/10.30525/978-9934-26-151-0-22.

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This study is devoted to a generalized analysis of modern public processes for the formation of new mechanisms of crisis management and the regional management reform practice in the field of education, which are gradually carried out in the context of general reform and new strategies. The special relevance of modern education management practices is noted, among which are formation of new management concepts in the conditions of emergencies and crisis; redistribution of areas of responsibility between the region and territorial communities; revival of partnership interaction of public authorities with civil society institutions; implementation of state policy through new contexts of forming a network of educational institutions, resource provision and budgeting; strategy for the development of the educational sphere through the implementation of investment infrastructure projects; expanding the scope of educational services in accordance with community requests, etc. European experience in active decentralization, which will stimulate sustainable community development, successful overcoming of crises and building a strategy for regional development shows the development of a systematic vision of the local situation in education and other social spheres that prevent socio-economic crises; active and effective communicative action “state-community”, which is aimed at deep democracy, sustainable development, unity, transparency of public institutions; humancenteredness on the basis of social democracy, involvement of citizens in various government procedures in communities; impact on the sustainable development of local democracy and financial self-sufficiency of the community; sustainable development of the public sector of the community, improving the quality of life / activities / human education. The basis of public activity of the authorities is a consistent communicative action that will promote the in-depth development of mutual trust, openness and efficiency. The subject of the study was the management vertical of the region and education management. The research methodology can be outlined as follows: understanding and worldview, which outline the operating environment of self-discipline analysis, forecasting, modeling, diagnosis and work with information, models, algorithms, cognitive, practical and evaluative, which complement each other in real life. The purpose of the study: to systematically generalize modern management processes of public authorities in anti-crisis strategies and new relevant mechanisms of organizational activities of public institutions of Dnipropetrovsk region, which are aimed at sustainable development of society and man, soft overcoming of growth problems through mechanisms of organizational and managerial overcoming of educational crises. areas in the region. The study found that the systematic activities of public institutions in the region, models of involvement of public institutions contribute to sustainable community development and form effective resilience to crises, restore confidence in government by citizens, improve the quality of local infrastructure projects in education, strengthen positive social trends – economic indicators of the community.
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Conference papers on the topic "Generalized trust region subproblem"

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Bienstock, Daniel, and Alexander Michalka. "Polynomial Solvability of Variants of the Trust-Region Subproblem." In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973402.28.

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Lu, H., G. Gao, H. Florez, J. Vink, C. Blom, T. Wells, and F. Saaf. "Solving Gauss-Newton Trust Region Subproblem with Bound Constraints." In ECMOR 2022. European Association of Geoscientists & Engineers, 2022. http://dx.doi.org/10.3997/2214-4609.202244007.

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Han, Jeongwoo, and Panos Papalambros. "A Sequential Linear Programming Coordination Algorithm for Analytical Target Cascading." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35361.

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Decomposition-based strategies, such as analytical target cascading (ATC), are often employed in design optimization of complex systems. Achieving convergence and computational efficiency in the coordination strategy that solves the partitioned problem is a key challenge. A new convergent strategy is proposed for ATC, which coordinates the interactions among subproblems using sequential lineralizations. Linearity of subproblems is maintained using L∞ norms to measure deviations between targets and responses. A subproblem suspension strategy is used to temporarily suspend inclusion of subproblems that do not need significant redesign, based on trust region and target value step size. The proposed strategy is intended for use in optimization problems where sequential linearizations are typically effective, such as problems with extensive monotonicities, large number of constraints relative to variables, and propagation of probabilities with normal distributions. Experiments with test problems show that, relative to standard ATC coordination, the number of subproblem evaluations is reduced considerably while maintaining accuracy.
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Alpak, Faruk, Yixuan Wang, Guohua Gao, and Vivek Jain. "Benchmarking and Field-Testing of the Distributed Quasi-Newton Derivative-Free Optimization Method for Field Development Optimization." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206267-ms.

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Abstract Recently, a novel distributed quasi-Newton (DQN) derivative-free optimization (DFO) method was developed for generic reservoir performance optimization problems including well-location optimization (WLO) and well-control optimization (WCO). DQN is designed to effectively locate multiple local optima of highly nonlinear optimization problems. However, its performance has neither been validated by realistic applications nor compared to other DFO methods. We have integrated DQN into a versatile field-development optimization platform designed specifically for iterative workflows enabled through distributed-parallel flow simulations. DQN is benchmarked against alternative DFO techniques, namely, the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method hybridized with Direct Pattern Search (BFGS-DPS), Mesh Adaptive Direct Search (MADS), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). DQN is a multi-thread optimization method that distributes an ensemble of optimization tasks among multiple high-performance-computing nodes. Thus, it can locate multiple optima of the objective function in parallel within a single run. Simulation results computed from one DQN optimization thread are shared with others by updating a unified set of training data points composed of responses (implicit variables) of all successful simulation jobs. The sensitivity matrix at the current best solution of each optimization thread is approximated by a linear-interpolation technique using all or a subset of training-data points. The gradient of the objective function is analytically computed using the estimated sensitivities of implicit variables with respect to explicit variables. The Hessian matrix is then updated using the quasi-Newton method. A new search point for each thread is solved from a trust-region subproblem for the next iteration. In contrast, other DFO methods rely on a single-thread optimization paradigm that can only locate a single optimum. To locate multiple optima, one must repeat the same optimization process multiple times starting from different initial guesses for such methods. Moreover, simulation results generated from a single-thread optimization task cannot be shared with other tasks. Benchmarking results are presented for synthetic yet challenging WLO and WCO problems. Finally, DQN method is field-tested on two realistic applications. DQN identifies the global optimum with the least number of simulations and the shortest run time on a synthetic problem with known solution. On other benchmarking problems without a known solution, DQN identified compatible local optima with reasonably smaller numbers of simulations compared to alternative techniques. Field-testing results reinforce the auspicious computational attributes of DQN. Overall, the results indicate that DQN is a novel and effective parallel algorithm for field-scale development optimization problems.
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Gao, Guohua, Horacio Florez, Sean Jost, Shakir Shaikh, Kefei Wang, Jeroen Vink, Carl Blom, Terence Wells, and Fredrik Saaf. "Implementation of Asynchronous Distributed Gauss-Newton Optimization Algorithms for Uncertainty Quantification by Conditioning to Production Data." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210118-ms.

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Abstract Previous implementation of distributed Gauss-Newton (DGN) optimization algorithm runs multiple optimization threads in parallel, employing a synchronous running mode (S-DGN). As a result, it waits for all simulations submitted in each iteration to complete, which may significantly degrade performance because a few simulations may run much longer than others, especially for time-consuming real-field cases. To overcome this limitation and thus improve the DGN optimizer's execution, we propose two asynchronous DGN (A-DGN) optimization algorithms in this paper. The A-DGN optimizer is a well-parallelized and efficient derivative-free (DFO) method. The A-DGN optimizer generates multiple initial guesses by sampling from the prior probability distribution of uncertain parameters in the first iteration. It then runs multiple simulations on high-performance-computing (HPC) clusters in parallel. A checking time interval is introduced to control the optimization process. The A-DGN optimizer checks the status of all running simulations after every checking time frame. A new simulation case is proposed immediately once the simulation of an optimization thread is completed, without waiting for the completion of other simulations. Thus, each A-DGN optimization thread becomes independent. The two A-DGN optimization algorithms are 1) the local-search algorithm to locate multiple maximum-a-posteriori (MAP) estimates and 2) the integrated global-search algorithm with the randomized-maximum-likelihood (RML) method to generate hundreds of RML samples in parallel for uncertainty quantification. We modified the training-data data set updating algorithm using the iteration index for each thread to implement the asynchronous running mode. The sensitivity matrix at the best solution of each optimization thread is estimated by linear interpolation of a subset of the training data closest to the best solution, using the modified QR decomposition method. A new simulation case (or search point) is generated by solving the Gauss-Newton trust-region subproblem (GNTRS), together with the estimated sensitivity matrix, using the more efficient and robust GNTRS solver that we developed recently. The proposed A-DGN optimization method is tested and validated on a synthetic problem and then applied to a real-field deep-water reservoir model. Numerical tests confirm that the proposed A-DGN optimization method can converge to solutions with matching quality comparable to those obtained by the S-DGN optimizer, saving on the time required for the optimizer to converge by a factor ranging from 1.3 to 2 when compared to the S-DGN optimizer depending on the problem. The new A-DGN optimization algorithm presented in this paper helps improve efficiency and robustness in solving history-matching or inversion problems, especially for uncertainty quantification of subsurface model parameters and production forecasts of real-field reservoirs by conditioning to production data.
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Reports on the topic "Generalized trust region subproblem"

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Santos, Sandra A., and Danny C. Sorensen. A New Matrix-Free Algorithm for the Large-Scale Trust-Region Subproblem. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada445632.

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Balza, Lenin, Lina M. Díaz, Nicolás Gómez Parra, and Osmel Manzano. The Unwritten License: The Social License to Operate in Latin America's Extractive Sector. Inter-American Development Bank, December 2021. http://dx.doi.org/10.18235/0003820.

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The Latin America and the Caribbean region has benefited significantly from economic growth driven by the extractive sector. At the same time, the region has experienced high levels of conflicts related to this sector. This paper presents an overview of citizens' perceptions of the extractive industries in Bolivia, Colombia, Ecuador, Peru, and Venezuela. Using a representative sample for each country, we identify regional and country-specific determinants of the Social License to Operate (SLO). The SLO is an unwritten license of social approval accorded to extractive projects by citizens. In this paper, we investigate a generalized version of the SLO, capturing public sentiment toward the mining and the oil and gas sectors in general. While our findings confirm that perceptions vary across countries, we show that governance is the strongest predictor of trust between citizens and the extractive sector, which is consistent with the evidence in the literature. In addition, procedural justice, distributive justice, and nationalism play essential roles in shaping individuals' attitudes. These findings suggest that strengthening government institutions could contribute to the prevention of conflict around extractive industries.
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