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Relevant bibliographies by topics / Unconstraint optimization

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Academic literature on the topic 'Unconstraint optimization'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Unconstraint optimization"

1

Hatamlou, Abdolreza. "Numerical Optimization Using the Heart Algorithm." International Journal of Applied Evolutionary Computation 9, no. 2 (2018): 33–37. http://dx.doi.org/10.4018/ijaec.2018040103.

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In this article the authors investigate the application of the heart algorithm for solving unconstraint numerical optimization problems. Heart algorithms are a novel optimization algorithm which mimics the heart function and circulatory system procedure in the human beings. It starts with a number of candidate solutions for the given problem and utilizes the contraction and expansion actions to move the candidates in the search space for finding optimal solution. The applicability and performance of the heart algorithm for solving unconstrained optimization problems has been tested using sever

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YU, GUOLIN. "OPTIMALITY OF GLOBAL PROPER EFFICIENCY FOR CONE-ARCWISE CONNECTED SET-VALUED OPTIMIZATION USING CONTINGENT EPIDERIVATIVE." Asia-Pacific Journal of Operational Research 30, no. 03 (2013): 1340004. http://dx.doi.org/10.1142/s0217595913400046.

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This note deals with the optimality conditions of set-valued unconstraint optimization problem in real normed linear spaces. Based upon the concept of contingent epiderivative, the unified necessary and sufficient optimality conditions for global proper efficiency in vector optimization problem involving cone-arcwise connected set-valued mapping are presented.

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Prajapati, Raju, and Om Prakash Dubey. "ANALYSING THE IMPACT OF PENALTY CONSTANT ON PENALTY FUNCTION THROUGH PARTICE SWARM OPTIMIZATION." International Journal of Students' Research in Technology & Management 6, no. 2 (2018): 01–06. http://dx.doi.org/10.18510/ijsrtm.2018.621.

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Non Linear Programming Problems (NLPP) are tedious to solve as compared to Linear Programming Problem (LPP). The present paper is an attempt to analyze the impact of penalty constant over the penalty function, which is used to solve the NLPP with inequality constraint(s). The improved version of famous meta heuristic Particle Swarm Optimization (PSO) is used for this purpose. The scilab programming language is used for computational purpose. The impact of penalty constant is studied by considering five test problems. Different values of penalty constant are taken to prepare the unconstraint NL

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Vo, Duc Thinh, and Ngoc Cam Huynh. "<span><strong>Subdifferentials with degrees of freedom and applications to optimization problems</strong></span>." Dong Thap University Journal of Science 14, no. 5 (2024): 12–19. https://doi.org/10.52714/dthu.14.5.2025.1401.

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In this work, we first present a new class of generalized differentials, namely subdifferentials with degrees of freedom as well as their applications in nonsmooth optimization problems. We then establish some computation rules for subdifferentials with degree of frecdom of functions under basic qualification constraints. By using these computation rules, we provide necessary and sufficient conditions for unconstraint optmization problems and for optimization problems with geometric constraints.

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S. Younis, Maha, and Basma Tareq. "A New Hybrid Conjugates Gradient Algorithm for Unconstraint Optimization Problems." International Journal of Engineering Technology and Natural Sciences 4, no. 1 (2022): 81–94. http://dx.doi.org/10.46923/ijets.v4i1.148.

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In this paper, we present a new hybrid conjugate gradient strategy that is both efficient and effective for solving unconstrained optimization problems. The parameter is derived from a convex combination of the and the conjugate gradient methods. We demonstrated that this strategy is globally convergent under strong Wolfe line search conditions, and that the recommended hybrid CG method is capable of creating a descending search direction at each iteration. Numerical results are presented in this study, demonstrating that the proposed technique is both efficient and promising.

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Jiang, Zhi Xia, Pin Chao Meng, Yan Zhong Li, and Wei Shi Yin. "Collaborative Optimization Algorithm Based on the Penalty Function." Applied Mechanics and Materials 538 (April 2014): 447–50. http://dx.doi.org/10.4028/www.scientific.net/amm.538.447.

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The paper discusses the collaborative optimization problems with bounded. Based the penalty function the system-level optimization convert to a unconstraint programming. To the discipline-level optimization, the normalized weighted coefficients are used and combine relaxation factors to solve. It uses the relaxation factor to expand the feasible region, and possibly makes the iteration in the calculation process run inside feasible region. The data have shown that the algorithm has expanded the choice range of the initial points with high calculation accuracy and better algorithm stability.

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Solaimani, S., and P. Arul. "Unconstraint Optimal Power Flow using Improved Cuckoo Search Algorithm." International Journal of Advance Research and Innovation 5, no. 2 (2017): 102–7. http://dx.doi.org/10.51976/ijari.521718.

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This paper presents an efficient and reliable a swarm-Intelligence based algorithm and bio-Inspired algorithm approach to unconstraint obtain optimal power flow (OPF) problem solution. This approach employs a nature inspired meta-heuristics optimization algorithm such as improved cuckoo search algorithm to determine the optimal setting of control variable. The performance of the improved cuckoo search algorithm (ICS) is examined and tested on IEEE 30 bus test system with objective function is minimization of fuel cost. The solution is done using MATLAB software.

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Hassan, Basim A. "A New Hybrid Conjugate Gradient Method with Guaranteed Descent for Unconstraint Optimization." Al-Mustansiriyah Journal of Science 28, no. 3 (2018): 193. http://dx.doi.org/10.23851/mjs.v28i3.114.

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The conjugate gradient method an efficient technique for solving the unconstrained optimization problem. In this paper, we propose a new hybrid nonlinear conjugate gradient methods, which have the descent at every iteration and globally convergence properties under certain conditions. The numerical results show that new hybrid method are efficient for the given test problems.

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Hady, Mohammed M. Abdel, and Maha S. Younis. "New Parameter of CG-Method with Exact Line Search for Unconstraint Optimization." OALib 07, no. 04 (2020): 1–8. http://dx.doi.org/10.4236/oalib.1106236.

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Bayati. "New Scaled Sufficient Descent Conjugate Gradient Algorithm for Solving Unconstraint Optimization Problems." Journal of Computer Science 6, no. 5 (2010): 511–18. http://dx.doi.org/10.3844/jcssp.2010.511.518.

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Dissertations / Theses on the topic "Unconstraint optimization"

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Kroyan, Julia. "Trust-search algorithms for unconstrained optimization /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3120456.

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Erway, Jennifer B. "Iterative methods for large-scale unconstrained optimization." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3222051.

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Thesis (Ph. D.)--University of California, San Diego, 2006.<br>Title from first page of PDF file (viewed September 20, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 146-149).

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Abd, Al Mahamad N. H. "Conjugate gradient type methods for unconstrained optimization." Thesis, University of Leeds, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329507.

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Anthony, Tim. "On the Topic of Unconstrained Black-Box Optimization with Application to Pre-Hospital Care in Sweden : Unconstrained Black-Box Optimization." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185718.

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In this thesis, the theory and application of black-box optimization methods are explored. More specifically, we looked at two families of algorithms, descent methods andresponse surface methods (closely related to trust region methods). We also looked at possibilities in using a dimension reduction technique called active subspace which utilizes sampled gradients. This dimension reduction technique can make the descent methods more suitable to high-dimensional problems, which turned out to be most effective when the data have a ridge-like structure. Finally, the optimization methods were used

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Xie, Xiaohui. "A matrix free method for unconstrained optimization problems." HKBU Institutional Repository, 2011. http://repository.hkbu.edu.hk/etd_ra/1281.

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Kockesen, Kerem Talip. "Evolutionary Algorithms For Deterministic And Stochastic Unconstrained Function Optimization." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605583/index.pdf.

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Most classical unconstrained optimization methods require derivative information. Different methods have been proposed for problems where derivative information cannot be used. One class of these methods is heuristics including Evolutionary Algorithms (EAs). In this study, we propose EAs for unconstrained optimization under both deterministic and stochastic environments. We design a crossover operator that tries to lead the algorithm towards the global optimum even when the starting solutions are far from the optimal solution. We also adapt this algorithm to a stochastic environment where ther

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Gertz, Edward Michael. "Combination trust-region line-search methods for unconstrained optimization /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1998. http://wwwlib.umi.com/cr/ucsd/fullcit?p9935439.

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Alias, Abbas Younis. "New combined Conjugate Gradient and Variable Metric methods for unconstrained optimization." Thesis, University of Leeds, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329233.

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Mohr, Darin Griffin. "Hybrid Runge-Kutta and quasi-Newton methods for unconstrained nonlinear optimization." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/1249.

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Finding a local minimizer in unconstrained nonlinear optimization and a fixed point of a gradient system of ordinary differential equations (ODEs) are two closely related problems. Quasi-Newton algorithms are widely used in unconstrained nonlinear optimization while Runge-Kutta methods are widely used for the numerical integration of ODEs. In this thesis, hybrid algorithms combining low-order implicit Runge-Kutta methods for gradient systems and quasi-Newton type updates of the Jacobian matrix such as the BFGS update are considered. These hybrid algorithms numerically approximate the gradient

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Ng, Chi Kong. "Globally convergent and efficient methods for unconstrained discrete-time optimal control." HKBU Institutional Repository, 1998. http://repository.hkbu.edu.hk/etd_ra/149.

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Books on the topic "Unconstraint optimization"

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Ram, Bhagwat, Shashi Kant Mishra, Kin Keung Lai, and Predrag Rajković. Unconstrained Optimization and Quantum Calculus. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2435-2.

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Punnen, Abraham P., ed. The Quadratic Unconstrained Binary Optimization Problem. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04520-2.

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Mishra, Shashi Kant, and Bhagwat Ram. Introduction to Unconstrained Optimization with R. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0894-3.

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Freeman, T. L. Parallel projected variable metric algorithms for unconstrained optimization. Institute for Computer Applications in Science and Engineering, 1989.

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Andrei, Neculai. Nonlinear Conjugate Gradient Methods for Unconstrained Optimization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42950-8.

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Fiacco, Anthony V. Nonlinear programming: Sequential unconstrained minimization techniques. Society for Industrial and Applied Mathematics, 1990.

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7

M, Gould N. I., Rutherford Appleton Laboratory, and Council For The Central Laboratory of The Research Councils., eds. A linesearch algorithm with memory for unconstrained optimization. Rutherford Appleton Laboratory, 1998.

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Freeman, T. L. Parallel projected variable metric algorithms for unconstrained optimization. National Aeronautics and Space Administration, Langley Research Center, 1990.

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9

Dennis, J. E. Numerical methods for unconstrained optimization and nonlinear equations. Society for Industrial and Applied Mathematics, 1996.

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10

D, Meegan, and Sprevak D, eds. An introduction to unconstrained optimisation. A. Hilger, 1990.

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Book chapters on the topic "Unconstraint optimization"

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Eiselt, H. A., and Carl-Louis Sandblom. "Unconstrained Optimization." In Nonlinear Optimization. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19462-8_2.

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Polak, Elijah. "Unconstrained Optimization." In Applied Mathematical Sciences. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-0663-7_1.

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Wisniewski, Mik. "Unconstrained optimization." In Mathematics for Economics. Macmillan Education UK, 2013. http://dx.doi.org/10.1007/978-1-137-01546-4_20.

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Sofer, Ariela. "Unconstrained Optimization." In Encyclopedia of Operations Research and Management Science. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1083.

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Faigle, Ulrich, Walter Kern, and Georg Still. "Unconstrained Optimization." In Kluwer Texts in the Mathematical Sciences. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-015-9896-5_11.

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Haftka, Raphael T., Zafer Gürdal, and Manohar P. Kamat. "Unconstrained Optimization." In Elements of Structural Optimization. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-015-7862-2_4.

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Abidi, Mongi A., Andrei V. Gribok, and Joonki Paik. "Unconstrained Optimization." In Advances in Computer Vision and Pattern Recognition. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46364-3_4.

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Haftka, Raphael T., and Zafer Gürdal. "Unconstrained Optimization." In Elements of Structural Optimization. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2550-5_4.

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Ponce-Ortega, José María, Rogelio Ochoa-Barragán, and César Ramírez-Márquez. "Unconstrained Optimization." In Optimization of Chemical Processes. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57270-8_2.

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Li, PhD, Haksun. "Unconstrained Optimization." In Numerical Methods Using Kotlin. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8826-9_9.

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Conference papers on the topic "Unconstraint optimization"

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Beals, Nathan. "Aerodynamic Optimization of a Small UAS Rotor for a Mission with Hover and Forward-Flight Segments." In Vertical Flight Society 74th Annual Forum & Technology Display. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12716.

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Aerodynamic optimizations of small rotors for use on a quadrotor vehicle are presented using both axial and forward flight blade element momentum theory to calculate the vehicle performance over a typical intelligence, surveillance, and reconnaissance (ISR) mission. Total energy required is minimized by allowing the rotor chord distribution, twist distribution, and hover rotational speed to vary subject to several geometric and performance constraints. The global minimum of the objective function is found using the non-gradient-based genetic algorithm NSGA-II. The main design case is a typical

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Yang, Shuangye, Benyu Wang, Liangbo Hu, et al. "A conjugate gradient method for solving unconstrained optimization problems." In 2024 6th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP). IEEE, 2024. https://doi.org/10.1109/icmsp64464.2024.10867051.

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Ahmed, Myasar A., and Maha S. Younis. "A new hybrid conjugate gradient method for unconstraint optimization." In 1ST SAMARRA INTERNATIONAL CONFERENCE FOR PURE AND APPLIED SCIENCES (SICPS2021): SICPS2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0121648.

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Maleki, Hoda. "Multi-Objective Optimization of an Axial Compressor." In ASME 2008 Fluids Engineering Division Summer Meeting collocated with the Heat Transfer, Energy Sustainability, and 3rd Energy Nanotechnology Conferences. ASMEDC, 2008. http://dx.doi.org/10.1115/fedsm2008-55015.

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The axial compressor is one of the most challenging components in aero-engine system and design; because of that finding an optimum design for this component is very important and valuable in the field. In this study, the objective functions selected to apply optimization method are the stage efficiency which leads to minimum losses, the stall margin which allows a wide stable characteristics curve and the inlet stage specific area which is related to the weight of a stage. In any case, the weight reduction inevitably causes loss of efficiency, and the higher efficiency will lead to decrease o

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Abeynanda, Hansi, and G. H. Jayantha Lanel. "Convergence of Gradient Methods with Deterministic and Bounded Noise." In SLIIT INTERNATIONAL CONFERENCE ON ADVANCEMENTS IN SCIENCES AND HUMANITIES [SICASH]. Faculty of Humanities and Sciences, SLIIT, 2022. http://dx.doi.org/10.54389/rccw7413.

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In this paper, we analyse the effects of noise on the gradient methods for solving a convex unconstraint optimization problem. Assuming that the objective function is with Lipschitz continuous gradients, we analyse the convergence properties of the gradient method when the noise is deterministic and bounded. Our theoretical results show that the gradient algorithm converges to the related optimality within some tolerance, where the tolerance depends on the underlying noise, step size, and the gradient Lipschitz continuity constant of the underlying objective function. Moreover, we consider an

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Stanimirović, Predrag S. "Neutrosophy in Unconstrained Nonlinear Optimization." In International Workshop “Hybrid methods of modeling and optimization in complex systems”. European Publisher, 2023. http://dx.doi.org/10.15405/epct.23021.17.

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Zhigang Zhou. "Modified particle swarm optimization for unconstrained optimization." In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccae.2010.5451219.

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Bianchi, Pascal, and Jérémie Jakubowicz. "Distributed Stochastic Optimization for Constrained and Unconstrained Optimization." In 5th International ICST Conference on Performance Evaluation Methodologies and Tools. ACM, 2011. http://dx.doi.org/10.4108/icst.valuetools.2011.245895.

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Ibrahim, Abdelhameed, Hesham Arafat Ali, Marwa M. Eid, and El-Sayed M. El-kenawy. "Chaotic Harris Hawks Optimization for Unconstrained Function Optimization." In 2020 16th International Computer Engineering Conference (ICENCO). IEEE, 2020. http://dx.doi.org/10.1109/icenco49778.2020.9357403.

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Huang, Zhiyong, Youlin Shang, and Fengye Wang. "Transformation Function Method for Unconstrained Global Optimization." In 2012 Fifth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2012. http://dx.doi.org/10.1109/cso.2012.75.

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Reports on the topic "Unconstraint optimization"

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Dennis, J. E., Schnabel Jr., and Robert B. A View of Unconstrained Optimization. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada188327.

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Schnabel, Robert B. Sequential and Parallel Methods for Unconstrained Optimization. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada203807.

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Byrd, Richard H., Robert B. Schnabel, and Gerald A. Shultz. Parallel Quasi-Newton Methods for Unconstrained Optimization. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada193250.

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Bouaricha, A. Tensor methods for large, sparse unconstrained optimization. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/409872.

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Schnabel, Robert B., and Ta-Tung Chow. Tensor Methods for Unconstrained Optimization Using Second Derivatives. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada213642.

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Chow, Ta-Tung, Elizabeth Eskow, and Robert B. Schnabel. A Software Package for Unconstrained Optimization Using Tensor Methods. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada233989.

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Dennis, J. E., N. Echebest, M. T. Guardarucci, J. M. Martinez, H. D. Scolnik, and C. Vaccino. A Curvilinear Search Using Tridiagonal Secant Updates for Unconstrained Optimization. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada455265.

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Bouaricha, A. STENMIN: A software package for large, sparse unconstrained optimization using tensor methods. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/399726.

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Chen, Yunmei, Guanghui Lan, Yuyuan Ouyang, and Wei Zhang. Fast Bundle-Level Type Methods for Unconstrained and Ball-Constrained Convex Optimization. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada612792.

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Torczon, Virginia. PDS: Direct Search Methods for Unconstrained Optimization on Either Sequential or Parallel Machines. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada455473.

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