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Solving nonconvex climate control problems: pitfalls and algorithm performances

Carmen G. Moles, Julio R. Banga, Klaus Keller
2004 Applied Soft Computing  
As an illustrative example, we consider the optimal control problem of choosing the optimal greenhouse gas emissions abatement to avoid or delay abrupt and irreversible climate damages.  ...  In this contribution, we explore numerical solution techniques for nonconvex and nondifferentiable economic optimal growth models.  ...  Lieber, Julio R. 555 Banga, Klaus Keller, Global optimization of climate 556 control problems using evolutionary and stochastic 557 algorithms, in: J.M. Benitez, O. Cordon, F. Hoffmann, 558 R.  ... 
doi:10.1016/j.asoc.2004.03.011 fatcat:qjn6xbt7evejphnkep6fzih3ju

Scope for the Application of Mathematical Programming Techniques in the Synthesis and Planning of Sustainable Processes [chapter]

Ignacio Grossmann, Gonzalo Guillén-Gosálbez
2009 Design for Energy and the Environment  
Gonzalo Guillén-Gosálbez wishes to acknowledge support from the Spanish Ministry of Education and Science (research project DPI2008-04099). references  ...  Such problems are extremely difficult to solve since the expected recourse function is discontinuous and nonconvex (Sahinidis, 2004) .  ...  The problem was solved within 2% tolerance of the upper and lower bounds with the proposed method.  ... 
doi:10.1201/9781439809136-c5 fatcat:ynigus6cyjbntnqba4hoffpqui

Scope for the application of mathematical programming techniques in the synthesis and planning of sustainable processes

Ignacio E. Grossmann, Gonzalo Guillén-Gosálbez
2010 Computers and Chemical Engineering  
Gonzalo Guillén-Gosálbez wishes to acknowledge support from the Spanish Ministry of Education and Science (research project DPI2008-04099). references  ...  Such problems are extremely difficult to solve since the expected recourse function is discontinuous and nonconvex (Sahinidis, 2004) .  ...  The problem was solved within 2% tolerance of the upper and lower bounds with the proposed method.  ... 
doi:10.1016/j.compchemeng.2009.11.012 fatcat:os2o2wyufbdjvkcgb4laumvdwq

Adjoint Methods in Computational Science, Engineering, and Finance (Dagstuhl Seminar 14371)

Nicolas R. Gauger, Michael Giles, Max Gunzburger, Uwe Naumann, Marc Herbstritt
2015 Dagstuhl Reports  
The development of adjoint numerical methods yields a large number of theoretical, algorithmic, and practical (implementation) challenges most of them to be addressed by state of the art Computer Science  ...  / analysis, and functional analysis.  ...  Thus the problem of finding the gradients and adjoints is solved. We will illustrate the approach on 4 semi-academic mean-field type control problems  ... 
doi:10.4230/dagrep.4.9.1 dblp:journals/dagstuhl-reports/GaugerGGN14 fatcat:5aio2bo5g5bnxlymnn63ktwj7y

Variable projection methods for an optimized dynamic mode decomposition [article]

Travis Askham, J. Nathan Kutz
2017 arXiv   pre-print
By making use of the variable projection method for nonlinear least squares problems, the algorithm is capable of solving the underlying nonlinear optimization problem efficiently.  ...  We explore the performance of the algorithm with some numerical examples for synthetic and real data from dynamical systems and find that the resulting decomposition displays less bias in the presence  ...  JNK acknowledges helpful and insightful conversations with Steven Brunton, Bingni Brunton, Joshua Proctor, Jonathan Tu and Clarence Rowley.  ... 
arXiv:1704.02343v1 fatcat:lhlkp3xd5zhxbpon46thaap3pq

Bilevel optimization to deal with demand response in power grids: models, methods and challenges

Carlos Henggeler Antunes, Maria João Alves, Billur Ecer
2020 TOP - An Official Journal of the Spanish Society of Statistics and Operations Research  
The hierarchical nature of the interaction between decision-makers controlling different sets of variables in several problems involving demand response is highlighted, which establishes bilevel optimization  ...  The main concepts and solution approaches to those problems are underlined, in the context of the theoretical, methodological, and computational issues associated with bilevel optimization.  ...  for Science and Technology and Regional Operational Program  ... 
doi:10.1007/s11750-020-00573-y fatcat:tustla2akbajjdx7pjcc5vul7m

Visible Light-Assisted Soft-Chemistry Route to Silver Nanomaterials at Room Temperature

Yi Wu, Xin Liu, Zi Huang, Xiang Fei He, Xi Yang, Qing Li
2019 Key Engineering Materials  
DTAB was used to perform as the surfactant. AgNPs were characterized with powder X-ray diffraction (XRD) and scanning electron microscope (SEM).  ...  The result showed that the nanoparticles are spherical and cube. The effect of temperature on the morphology and properties of silver nanoparticles was investigated.  ...  The algorithm for consensus control is performed also with JULIA programing language.  ... 
doi:10.4028/ fatcat:xara5sxzf5fsvbib2dy6vrl7j4

Patterns, predictions, and actions: A story about machine learning [article]

Moritz Hardt, Benjamin Recht
2021 arXiv   pre-print
Self-contained introductions to causality, the practice of causal inference, sequential decision making, and reinforcement learning equip the reader with concepts and tools to reason about actions and  ...  Throughout, the text discusses historical context and societal impact. We invite readers from all backgrounds; some experience with probability, calculus, and linear algebra suffices.  ...  Sampling is a difficult problem with numerous pitfalls that can strongly affect the performance of statistical estimators and the validity of what we learn from data.  ... 
arXiv:2102.05242v2 fatcat:wy47g4fojnfuxngklyewtjtqdi

Discovery of Physics from Data: Universal Laws and Discrepancies [article]

Brian M. de Silva
2020 arXiv   pre-print
Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles and governing equations from measurement data alone.  ...  By revisiting the classic problem of modeling falling objects of different size and mass, we highlight a number of nuanced issues that must be addressed by modern data-driven methods for automated physics  ...  for SLB and FA9550-17-1-0329 for JNK).  ... 
arXiv:1906.07906v3 fatcat:gjprqgcx3ffmjjjax7uiyhcgta

A data-driven peridynamic continuum model for upscaling molecular dynamics [article]

Huaiqian You, Yue Yu, Stewart Silling, Marta D'Elia
2021 arXiv   pre-print
Our two-dimensional tests show the robustness of the proposed algorithm on validation data sets that include thermal noise, different domain shapes and external loadings, and discretizations substantially  ...  To achieve this, we provide sufficient well-posedness conditions for discretized LPS models with sign-changing influence functions and develop a constrained optimization algorithm that minimizes the equation  ...  Acknowledgements Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell  ... 
arXiv:2108.04883v1 fatcat:hxzrqx2xordn7hnaw4avdb7qr4

Indirect Gaussian Graph Learning beyond Gaussianity [article]

Yiyuan She, Shao Tang, Qiaoya Zhang
2019 arXiv   pre-print
An iterative Gaussian graph learning algorithm is proposed with ease in implementation.  ...  Because of the nonconvex nature of the problem, studying the performance of the set of global minimizers may not provide enough guidance in practice.  ...  ., Hsieh et al. (2013) , Oztoprak et al. (2012) and Treister and Turek (2014) . All these algorithms can be seamlessly applied here to solve the 1 -penalized W -optimization problem.  ... 
arXiv:1610.02590v4 fatcat:yb35avfzbzggpbnrpovks5ruye

Data-driven identification of group dynamics for motion prediction and control

Mac Schwager, Carrick Detweiler, Iuliu Vasilescu, Dean M. Anderson, Daniela Rus
2008 Journal of Field Robotics  
Applications to livestock management are described, and the potential for surveillance, prediction, and control of various kinds of groups of dynamic agents are suggested. C  ...  The model combines effects from an agent's inertia, interactions between agents, and interactions between each agent and its environment.  ...  ) SWARMS (Scalable Swarms of Autonomous Robots and Mobile Sensors) project.  ... 
doi:10.1002/rob.20243 fatcat:nsxpqgqk7fgqtfdk5hbodk3xmm

Design and Maintenance Planning Problems in Commodity Distribution and Chemical Site Networks

Sreekanth Rajagopalan
In the first chapter, we introduce the turnaround planning problem and the challenges it poses in integrated sites. We also introduce the background for the network design problem.  ...  In the second problem, we investigate different optimization model formulations for the design of flow distribution networks where the flow is potential-driven and nonlinearly related to the potential  ...  Typical applications include in process synthesis, design and operations problems, circuit design, molecular design, mechanical and structural design problems, control problems, and parameter estimation  ... 
doi:10.1184/r1/6715784.v1 fatcat:3iet6qhp35ef5ndebsjxykukce

A survey on domain adaptation theory: learning bounds and theoretical guarantees [article]

Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani
2020 arXiv   pre-print
All famous machine learning algorithms that comprise both supervised and semi-supervised learning work well only under a common assumption: the training and test data follow the same distribution.  ...  We provide a first up-to-date description of existing results related to domain adaptation problem that cover learning bounds based on different statistical learning frameworks.  ...  the adaptation problem at hand can be solved efficiently.  ... 
arXiv:2004.11829v5 fatcat:hj6bxsrndjao3hfj5zi3yjl7q4

Modeling Knowledge: Model-based Decision Support and Soft Computations [chapter]

Marek Makowski, Andrzej P. Wierzbicki
2003 Studies in Fuzziness and Soft Computing  
Then the characteristics of models, and of modeling processes aimed at decision-making support for complex problems are presented.  ...  It starts with discussing basic elements of decision making process, including characteristics of complex decision problems, concepts of rationality, and various requirements for model-based support at  ...  Moreover, one uses for them efficient presolve algorithms, which greatly decrease the resources (time and memory) needed for solving large problems.  ... 
doi:10.1007/978-3-540-37008-6_1 fatcat:bpcxfegxxjhp5ojumajo625lwi
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