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Relevant bibliographies by topics / Mathematical optimization. Stochastic processes

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Academic literature on the topic 'Mathematical optimization. Stochastic processes'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Mathematical optimization. Stochastic processes"

1

Okur, Nurgul, Imdat Işcan, and Emine Yuksek Dizdar. "Hermite-Hadamard type inequalities for p-convex stochastic processes." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 9, no. 2 (2019): 148–53. http://dx.doi.org/10.11121/ijocta.01.2019.00602.

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In this study are investigated p-convex stochastic processes which are extensions of convex stochastic processes. A suitable example is also given for this process. In addition, in this case a p-convex stochastic process is increasing or decreasing, the relation with convexity is revealed. The concept of inequality as convexity has an important place in literature, since it provides a broader setting to study the optimization and mathematical programming problems. Therefore, Hermite-Hadamard type inequalities for p-convex stochastic processes and some boundaries for these inequalities are obta

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Fleming, Wendell H. "Max-plus stochastic processes." Applied Mathematics & Optimization 49, no. 2 (2004): 159–81. http://dx.doi.org/10.1007/bf02638150.

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Fleming, Wendell H. "Max-Plus Stochastic Processes." Applied Mathematics and Optimization 49, no. 2 (2004): 159–81. http://dx.doi.org/10.1007/s00245-003-0785-3.

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Cipra, Tomáš. "Autoregressive processes in optimization." Journal of Applied Probability 25, no. 2 (1988): 302–12. http://dx.doi.org/10.2307/3214438.

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Vector autoregressive processes of the first order are considered which are non-negative and optimize a linear objective function. These processes may be used in stochastic linear programming with a dynamic structure. By using Tweedie's results from the theory of Markov chains, conditions for geometric rates of convergence to stationarity (i.e. so-called geometric ergodicity) and for existence and geometric convergence of moments of these processes are obtained.

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Aase, Knut K. "Stochastic control of geometric processes." Journal of Applied Probability 24, no. 1 (1987): 97–104. http://dx.doi.org/10.2307/3214062.

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Stochastic optimization of semimartingales which permit a dynamic description, like a stochastic differential equation, leads normally to dynamic programming procedures. The resulting Bellman equation is often of a very genera! nature, and analytically hard to solve. The models in the present paper are formulated in terms of the relative change, and the optimality criterion is to maximize the expected rate of growth. We show how this can be done in a simple way, where we avoid using the Bellman equation. An application is indicated.

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Aase, Knut K. "Stochastic control of geometric processes." Journal of Applied Probability 24, no. 01 (1987): 97–104. http://dx.doi.org/10.1017/s0021900200030643.

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Stochastic optimization of semimartingales which permit a dynamic description, like a stochastic differential equation, leads normally to dynamic programming procedures. The resulting Bellman equation is often of a very genera! nature, and analytically hard to solve. The models in the present paper are formulated in terms of the relative change, and the optimality criterion is to maximize the expected rate of growth. We show how this can be done in a simple way, where we avoid using the Bellman equation. An application is indicated.

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7

Betounes, David, and Mylan Redfern. "Stochastic integrals for nonprevisible, multiparameter processes." Applied Mathematics & Optimization 28, no. 2 (1993): 197–223. http://dx.doi.org/10.1007/bf01182982.

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Ay, Nihat. "Locality of Global Stochastic Interaction in Directed Acyclic Networks." Neural Computation 14, no. 12 (2002): 2959–80. http://dx.doi.org/10.1162/089976602760805368.

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The hypothesis of invariant maximization of interaction (IMI) is formulated within the setting of random fields. According to this hypothesis, learning processes maximize the stochastic interaction of the neurons subject to constraints. We consider the extrinsic constraint in terms of a fixed input distribution on the periphery of the network. Our main intrinsic constraint is given by a directed acyclic network structure. First mathematical results about the strong relation of the local information flow and the global interaction are stated in order to investigate the possibility of controllin

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Beneš, Viktor. "On second-order formulas in anisotropic stereology." Advances in Applied Probability 27, no. 02 (1995): 326–43. http://dx.doi.org/10.1017/s0001867800026884.

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Formulas for anisotropic stereology of fibre and surface processes are presented. They concern the relation between second-order quantities of the original process and its projections and sections. Various mathematical tools for handling these formulas are presented, including stochastic optimization. Finally applications in stereology are discussed, relating to intensity estimators using anisotropic sampling designs. Variances of these estimators are expressed and evaluated for processes with the Poisson property.

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Beneš, Viktor. "On second-order formulas in anisotropic stereology." Advances in Applied Probability 27, no. 2 (1995): 326–43. http://dx.doi.org/10.2307/1427828.

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Formulas for anisotropic stereology of fibre and surface processes are presented. They concern the relation between second-order quantities of the original process and its projections and sections. Various mathematical tools for handling these formulas are presented, including stochastic optimization. Finally applications in stereology are discussed, relating to intensity estimators using anisotropic sampling designs. Variances of these estimators are expressed and evaluated for processes with the Poisson property.

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Dissertations / Theses on the topic "Mathematical optimization. Stochastic processes"

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Al-Mharmah, Hisham. "Global optimization of stochastic functions." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/25665.

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Huang, Hui. "Optimal control of piecewise continuous stochastic processes." Bonn : [s.n.], 1989. http://catalog.hathitrust.org/api/volumes/oclc/23831217.html.

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Yang, Yi. "Sequential convex approximations of chance constrained programming /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?IELM%202008%20YANG.

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Cheng, Tak Sum. "Stochastic optimal control in randomly-branching environments." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/713.

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Nascimento, Leite Andre. "Stochastic optimization approaches to open pit mine planning : applications for and the value of stochastic approaches." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=116039.

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The mine production schedule defines the sequence of extraction of selected mine units over the life of the mine, and consequentially establishes the ore supply and total material movement. This sequence should be optimized so as to maximize the overall discounted value of the project. Conventional schedule approaches are unable to incorporate grade uncertainty into the scheduling problem formulation and may lead to serious deviations from forecasted production targets. Stochastic mine production schedulers are considered to obtain more robust mine production schedule solutions.<br>The applica

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Zhou, Wei, and 周硙. "Topics in optimal stopping with applications in mathematical finance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46582046.

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Chen, Zhe Haykin Simon S. "Stochastic approaches for correlation-based learning." *McMaster only, 2004.

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Thompson, Mery H. "Optimum experimental designs for models with a skewed error distribution with an application to stochastic frontier models /." Connect to e-thesis, 2008. http://theses.gla.ac.uk/236/.

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Thesis (Ph.D.) - University of Glasgow, 2008.<br>Ph.D. thesis submitted to the Faculty of Information and Mathematical Sciences, Department of Statistics, 2008. Includes bibliographical references. Print version also available.

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Bouakiz, Mokrane. "Risk-sensitivity in stochastic optimization with applications." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/25457.

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Luedtke, James. "Integer Programming Approaches for Some Non-convex and Stochastic Optimization Problems." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19711.

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In this dissertation we study several non-convex and stochastic optimization problems. The common theme is the use of mixed-integer programming (MIP) techniques including valid inequalities and reformulation to solve these problems. We first study a strategic capacity planning model which captures the trade-off between the incentive to delay capacity installation to wait for improved technology and the need for some capacity to be installed to meet current demands. This problem is naturally formulated as a MIP with a bilinear objective. We develop several linear MIP formulations, along with c

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Books on the topic "Mathematical optimization. Stochastic processes"

1

Stochastic optimization methods. Springer, 2005.

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A, Zhilinskas, and SpringerLink (Online service), eds. Stochastic Global Optimization. Springer Science+Business Media,LLC, 2008.

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A, Zhilinskas, ed. Global optimization. Springer-Verlag, 1989.

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Spall, James C. Introduction to Stochastic Search and Optimization. John Wiley & Sons, Ltd., 2005.

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Optimization of stochastic models: The interface between simulation and optimization. Kluwer Academic, 1996.

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Zabinsky, Zelda B. Stochastic adaptive search for global optimization. Kluwer Academic Publishers, 2002.

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Stochastic adaptive search for global optimization. Kluwer Academic Publishers, 2003.

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8

K, Najim, ed. Learning automata and stochastic optimization. Springer, 1997.

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Poznyak, Alexander S. Learning automata and stochastic optimization. Springer, 1997.

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Rangaiah, Gade Pandu. Stochastic global optimization: Techniques and applications in chemical engineering. World Scientific, 2010.

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Book chapters on the topic "Mathematical optimization. Stochastic processes"

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Ferschl, Franz. "Stochastic Processes and Optimization Problems in Assemblage Systems." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-58201-1_17.

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Grindel, Ria, Wieger Hinderks, and Andreas Wagner. "Application of Continuous Stochastic Processes in Energy Market Models." In Mathematical Modeling, Simulation and Optimization for Power Engineering and Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62732-4_2.

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Balakrishnan, V. "Stochastic Processes." In Mathematical Physics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39680-0_21.

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Lax, Melvin. "Stochastic Processes." In Mathematical Tools for Physicists. Wiley-VCH Verlag GmbH & Co. KGaA, 2006. http://dx.doi.org/10.1002/3527607773.ch15.

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Kisielewicz, Michał. "Stochastic Processes." In Springer Optimization and Its Applications. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6756-4_1.

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Jost, Jürgen. "Stochastic Processes." In Mathematical Methods in Biology and Neurobiology. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6353-4_3.

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Wang, Shuming, and Junzo Watada. "Fuzzy Stochastic Renewal Processes." In Fuzzy Stochastic Optimization. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-9560-5_3.

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Gerke, Horst H., Youcef Kelanemer, Ulrich Hornung, Marián Slodička, and Stephan Schumacher. "Stochastic Optimization." In Optimal Control of Soil Venting: Mathematical Modeling and Applications. Birkhäuser Basel, 1999. http://dx.doi.org/10.1007/978-3-0348-8732-8_9.

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Durrett, Richard. "Mathematical Finance." In Essentials of Stochastic Processes. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45614-0_6.

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Durrett, Richard. "Mathematical Finance." In Essentials of Stochastic Processes. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3615-7_6.

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Conference papers on the topic "Mathematical optimization. Stochastic processes"

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Lupuleac, Sergey, Nadezhda Zaitseva, Maria Stefanova, et al. "Simulation and Optimization of Airframe Assembly Process." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87058.

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An approach for simulating the assembly process where compliant airframe parts are being joined by riveting is presented. The foundation of this approach is the mathematical model based on the reduction of the corresponding contact problem to a Quadratic Programming (QP) problem. The use of efficient QP algorithms enables mass contact problem solving on refined grids, which is needed for variation analysis and simulation as well as for the consequent assembly process optimization. To perform variation simulation, the initial gap between the parts is assumed to be stochastic and a cloud of such

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Choi, Seung-Kyum, Mervyn Fathianathan, and Dirk Schaefer. "Optimization of Complex Engineered Systems Under Risk and Uncertainty." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34678.

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The advances in information technology significantly impact the engineering design process. The primary objective of this research is to develop a novel probabilistic decision support tool to assist management of structural systems under risk and uncertainty by utilizing a stochastic optimization procedure and IT tools. The proposed mathematical and computational framework will overcome the drawbacks of the traditional methods and will be critically demonstrated through large-scale structural problems. The efficiency of the proposed procedure is achieved by the combination of the Karhunen-Loev

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Granita and Arifah Bahar. "Stochastic differential equation model to Prendiville processes." In THE 22ND NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM22): Strengthening Research and Collaboration of Mathematical Sciences in Malaysia. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4932498.

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PANFILO, G., P. TAVELLA, and C. ZUCCA. "STOCHASTIC PROCESSES FOR MODELLING AND EVALUATING ATOMIC CLOCK BEHAVIOUR." In Advanced Mathematical and Computational Tools in Metrology. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702647_0020.

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Kabanov, Yu M., B. L. Rozovskii, and A. N. Shiryaev. "Proceedings of Steklov Mathematical Institute Seminar; Statistics and Control of Stochastic Processes." In Steklov Mathematical Institute Seminar. WORLD SCIENTIFIC, 1997. http://dx.doi.org/10.1142/9789814529150.

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Scott, Michael J. "Quantifying Certainty in Design Decisions: Examining AHP." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dtm-34020.

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Quantification of stochastic uncertainty through confidence intervals when probability distributions are known is well-understood. There is considerable uncertainty in design decision support, however, for which probability distributions are unknown. The confidence interval formulation does not apply to these situations. The Analytic Hierarchy Process, or AHP, is an example of a tool with wide-spread industry application but questionable mathematical foundations. It is recognized by responsible practitioners that AHP should not be used as an optimization tool, but as a means of clarifying grou

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Kislukhin, V. V., and E. V. Kislukhina. "Laser-Doppler flow (LDF) and Heart rate intervals (R-R) as stochastic processes." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.9.

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Spivak, Semen, and Olga Kantor. "Mathematical Modeling and Optimization of Chemical and Technological Processes." In 2019 1st International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA). IEEE, 2019. http://dx.doi.org/10.1109/summa48161.2019.8947582.

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Poloskov, Igor E. "Transitional processes in linear stochastic parabolic and hyperbolic systems with constant delays." In 29TH RUSSIAN CONFERENCE ON MATHEMATICAL MODELLING IN NATURAL SCIENCES. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0059644.

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Petrovic, Luka, Juraj Persic, Marija Seder, and Ivan Markovic. "Stochastic Optimization for Trajectory Planning with Heteroscedastic Gaussian Processes." In 2019 European Conference on Mobile Robots (ECMR). IEEE, 2019. http://dx.doi.org/10.1109/ecmr.2019.8870970.

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Reports on the topic "Mathematical optimization. Stochastic processes"

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Perdigão, Rui A. P. Earth System Dynamic Intelligence - ESDI. Meteoceanics, 2021. http://dx.doi.org/10.46337/esdi.210414.

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Earth System Dynamic Intelligence (ESDI) entails developing and making innovative use of emerging concepts and pathways in mathematical geophysics, Earth System Dynamics, and information technologies to sense, monitor, harness, analyze, model and fundamentally unveil dynamic understanding across the natural, social and technical geosciences, including the associated manifold multiscale multidomain processes, interactions and complexity, along with the associated predictability and uncertainty dynamics. The ESDI Flagship initiative ignites the development, discussion and cross-fertilization of

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