The Conscientiousness × Interest Compensation (CONIC) model: Generalizability across domains, outcomes, and predictors.

2020 ◽  
Vol 112 (2) ◽  
pp. 271-287 ◽  
Author(s):  
Juyeon Song ◽  
Hanna Gaspard ◽  
Benjamin Nagengast ◽  
Ulrich Trautwein
Keyword(s):  
Conic Model

A Risk-Averse Conic Model for Networked Microgrids Planning With Reconfiguration and Reorganizations

2020 ◽  
Vol 11 (1) ◽  
pp. 696-709 ◽  
Author(s):  
Xiaoyu Cao ◽  
Jianxue Wang ◽  
Jianhui Wang ◽  
Bo Zeng
Keyword(s):  
Risk Averse ◽  
Conic Model ◽  
Networked Microgrids

Fitting Ellipse Based on the Dual Conic Model

2013 ◽  
Vol 670 ◽  
pp. 80-84
Author(s):  
C. Zhao ◽  
Mao Li Fu ◽  
J.T. Cheng
Keyword(s):  
Computational Efficiency ◽  
Dual Space ◽  
The Other ◽  
Dual Representation ◽  
Conic Model ◽  
Gradient Information ◽  
Edge Points ◽  
Low Sensitivity

This paper presents an operator that fitting the parameters of ellipses features, and improves the computational efficiency. Based on the dual conic model, this operator directly uses the raw gradient information in the neighborhood of an ellipse’s boundary, which use tangent lines and to apply the estimation in the dual space, so that avoiding the step of precisely extracting individual edge points. Moreover, under the dual representation, the dual conic can easily be constrained to a dual ellipse when minimizing the algebraic distance. The operator presents low sensitivity to noise and is compared to other estimation approaches, which shows good results, the accuracy of this operator is the highest, the time of this operator is moderate that compared with the other methods, and fall within acceptable levels, and in the case of noise or blur, the algorithm have shown very good robustness, in the practical environment can fit the parameters of the ellipse accurately.

A CONIC AFFINE SCALING DOGLEG METHOD FOR NONLINEAR OPTIMIZATION WITH BOUND CONSTRAINTS

2013 ◽  
Vol 30 (03) ◽  
pp. 1340011 ◽  
Author(s):  
LIJUAN ZHAO ◽  
WENYU SUN
Keyword(s):  
Quadratic Model ◽  
Affine Scaling ◽  
Bound Constraints ◽  
Siam Journal ◽  
Standard Testing ◽  
Conic Model ◽  
Mild Conditions ◽  
Numerical Tests ◽  
Collinear Scalings ◽  
Conic Approximations

The conic model proposed by Davidon (1980) [Davidon, WC (1980). Conic approximations and collinear scalings for optimizers. SIAM Journal on Numerical Analysis, 17, 268–281.] is an extension of quadratic model incorporating more information than quadratic model. In this paper, we propose a conic affine scaling dogleg method for bound constrained optimization. Under some mild conditions, the sequence generated by our algorithm converges to a stationary point. Numerical tests on a series of standard testing problems confirm the efficiency of the method.

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