scholarly journals Exploring Iterative Optimization Methods to Develop a MIMO Control Input.

2020 ◽  
Author(s):  
James Wilbanks ◽  
Brian Owens ◽  
Ryan Schultz
Keyword(s):  
Optimization Methods ◽  
Control Input ◽  
Iterative Optimization ◽  
Mimo Control

scholarly journals Exploring Iterative Optimization Methods to Develop a MIMO Control Input

2021 ◽  
pp. 1-17
Author(s):  
J. Justin Wilbanks ◽  
Ryan A. Schultz ◽  
Brian C. Owens
Keyword(s):  
Optimization Methods ◽  
Control Input ◽  
Iterative Optimization ◽  
Mimo Control

Comparison of Sensitivity Coefficient Calculation Methods in Automatic History Matching (includes associated paper 10873 )

1981 ◽  
Vol 21 (05) ◽  
pp. 551-557 ◽  
Author(s):  
Ali H. Dogru ◽  
John H. Seinfeld
Keyword(s):  
History Matching ◽  
Direct Method ◽  
Optimization Methods ◽  
Sensitivity Coefficient ◽  
Additional Time ◽  
Automatic History Matching ◽  
Matching Algorithm ◽  
Iterative Optimization ◽  
Incremental Change ◽  
Two Factors

Abstract The efficiency of automatic history matchingalgorithms depends on two factors: the computationtime needed per iteration and the number of iterations needed for convergence. In most historymatching algorithms, the most time-consumingaspect is the calculation of the sensitivitycoefficientsthe derivatives of the reservoir variables(pressure and saturation) with respect to the reservoirproperties (permeabilities and porosity). This paper presents an analysis of two methodsthe direct andthe variationalfor calculating sensitivitycoefficients, with particular emphasis on thecomputational requirements of the methods.If the simulator consists of a set of N ordinary differential equations for the grid-block variables(e.g., pressures)and there are M parameters forwhich the sensitivity coefficients are desired, the ratioof the computational efforts of the direct to thevariational method is N(M + 1)R = .N(N + 1) + M Thus, for M less than N the direct method is moreeconomical, whereas as M increases, a point isreached at which the variational method is preferred. Introduction There has been considerable interest in thedevelopment of automatic history matching algorithms.Although automatic history matching can offer significant advantages over trial-and-errorapproaches, its adoption has been somewhatlower than might have been anticipated when thefirst significant papers on the subject appeared. Oneobvious reason for the persistence of thetrial-and-error approach is that it does not requireadditional code development beyond that already involvedin the basic simulator, whereas automatic routinesrequire the appendixing of an iterative optimization routine to the basic simulator. Nevertheless, theinvestment of additional time in code developmentfor the history matching algorithm may be returned many fold during the actual history matchingexercise. In spite of the inherent advantages ofautomatic history matching, however, the automatic adjustment of the number of reservoir parameterstypically unknown even in a moderately sizedsimulation can require excessive amounts ofcomputation time. Therefore, it is of utmost importancethat an automatic history matching algorithm be asefficient as possible. Setting aside for the moment the issue of code complexity, the efficiency of analgorithm depends on two factors, the computationtime needed per iteration and the number ofiterations needed for convergence (whereconvergence is usually defined in terms of reaching acertain level of incremental change in either theparameters themselves or the objective function). Formost iterative optimization methods, the speed ofconvergence increases with the complexity of thealgorithm. SPEJ P. 551^

An Innovative Scheme to Make an Initial Guess for Iterative Optimization Methods to Calibrate Material Parameters of Strain-Hardening Elastoplastic Models

2021 ◽  
Author(s):  
Manouchehr Sanei ◽  
Philippe R. B. Devloo ◽  
Tiago L. D. Forti ◽  
Omar Durán ◽  
Erick S. R. Santos
Keyword(s):  
Strain Hardening ◽  
Optimization Methods ◽  
Initial Guess ◽  
Material Parameters ◽  
Iterative Optimization

Shaping the on-axis intensity profile of generalized Bessel beams by iterative optimization methods

2018 ◽  
Vol 20 (8) ◽  
pp. 085603 ◽  
Author(s):  
Runze Li ◽  
Xianghua Yu ◽  
Tong Peng ◽  
Yanlong Yang ◽  
Baoli Yao ◽  
...  
Keyword(s):  
Optimization Methods ◽  
Intensity Profile ◽  
Bessel Beams ◽  
Iterative Optimization

scholarly journals Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks

2021 ◽  
Vol 11 (9) ◽  
pp. 3822
Author(s):  
Simei Mao ◽  
Lirong Cheng ◽  
Caiyue Zhao ◽  
Faisal Nadeem Khan ◽  
Qian Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Silicon Photonics ◽  
Design Methodology ◽  
Degrees Of Freedom ◽  
Deep Neural Networks ◽  
Low Cost ◽  
Optimization Algorithms ◽  
Optimization Methods ◽  
Inverse Design ◽  
Iterative Optimization

Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices with similar structure much faster than iterative optimization methods and are thus suitable in situations where piles of optical components are needed. Finally, the applications of inverse design methodology in optical neural networks are also discussed. This review intends to provide the readers with the suggestion for the most suitable design methodology for a specific scenario.

scholarly journals On the best choice of a damping sequence in iterative optimization methods

1988 ◽  
Vol 32 ◽  
pp. 275-287
Author(s):  
L. N. Vaserstein
Keyword(s):  
Optimization Methods ◽  
Iterative Optimization

scholarly journals Performance Assessment of Iterative, Optimization and Non-Optimization Methods for Page Rank Aggregation

2020 ◽  
Vol 9 (3) ◽  
pp. 1884-1888
Keyword(s):  
Information Retrieval ◽  
Performance Assessment ◽  
Optimization Methods ◽  
Rank Aggregation ◽  
Page Rank ◽  
Standard Methods ◽  
Iterative Optimization ◽  
The Right ◽  
Degree Of Similarity

The annoyance of combining the ranked possibilities of many experts is an antique and particularly deep hassle that has won renewed importance in many machine getting to know, statistics mining, and information retrieval applications. Powerful rank aggregation turns into hard in actual-international situations in which the ratings are noisy, incomplete, or maybe disjoint. We cope with those difficulties by extending numerous standard methods of rank aggregation to do not forget similarity between gadgets within the diverse ranked Lists, further to their ratings. The intuition is that comparable items must obtain similar scores, given the right degree of similarity for the domain of hobby.

Optimization Methods in Finance

2018 ◽  
Author(s):  
Gérard Cornuéjols ◽  
Javier Peña ◽  
Reha Tütüncü
Keyword(s):  
Optimization Methods
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