scholarly journals A New Algorithm for Reducing Dimensionality of L1-CSVM Use Augmented Lagrange Method

2022 ◽  
Vol 10 (01) ◽  
pp. 21-30
Author(s):  
Mingzhu Cui ◽  
Liya Fan
Keyword(s):  
Lagrange Method ◽  
Augmented Lagrange Method

scholarly journals A note on embeddings for the Augmented Lagrange Method

2010 ◽  
Vol 20 (2) ◽  
pp. 183-196 ◽  
Author(s):  
Gemayqzel Bouza-Allende ◽  
Jurgen Guddat
Keyword(s):  
Augmented Lagrangian ◽  
Equality Constraints ◽  
Embedding Problem ◽  
Lagrange Method ◽  
Parametric Problem ◽  
Nonlinear Programs ◽  
Augmented Lagrange Method ◽  
Quadratic Perturbation ◽  
Lagrangian Embedding

Nonlinear programs (P) can be solved by embedding problem P into one parametric problem P(t), where P(1) and P are equivalent and P(0), has an evident solution. Some embeddings fulfill that the solutions of the corresponding problem P(t) can be interpreted as the points computed by the Augmented Lagrange Method on P. In this paper we study the Augmented Lagrangian embedding proposed in [6]. Roughly speaking, we investigated the properties of the solutions of P(t) for generic nonlinear programs P with equality constraints and the characterization of P(t) for almost every quadratic perturbation on the objective function of P and linear on the functions defining the equality constraints.

Small Infrared Target Detection Based on Low-Rank and Sparse Matrix Decomposition

2012 ◽  
Vol 239-240 ◽  
pp. 214-218 ◽  
Author(s):  
Cheng Yong Zheng ◽  
Hong Li
Keyword(s):  
Target Detection ◽  
Sparse Matrix ◽  
Infrared Image ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Target Component ◽  
Lagrange Method ◽  
Rank Matrix ◽  
Augmented Lagrange Method ◽  
Low Rank Matrix

Sparse and low-rank matrix decomposition (SLMD) tries to decompose a matrix into a low-rank matrix and a sparse matrix, it has recently attached much research interest and has good applications in many fields. An infrared image with small target usually has slowly transitional background, it can be seen as the sum of low-rank background component and sparse target component. So by SLMD, the sparse target component can be separated from the infrared image and then be used for small infrared target detection (SITD). The augmented Lagrange method, which is currently the most efficient algorithm used for solving SLMD, was applied in this paper for SITD, some parameters were analyzed and adjusted for SITD. Experimental results show our algorithm is fast and reliable.

Capacity optimization using augmented lagrange method in intelligent reflecting surface-based MIMO communication systems

2020 ◽  
Vol 17 (12) ◽  
pp. 123-138
Author(s):  
Daina Chang ◽  
Hao Jiang ◽  
Jie Zhou ◽  
Hongming Zhang ◽  
Mithun Mukherjee
Keyword(s):  
Communication Systems ◽  
Capacity Optimization ◽  
Lagrange Method ◽  
Mimo Communication ◽  
Augmented Lagrange Method ◽  
Reflecting Surface

Dynamic modeling and power optimization of a 4RPSP+PS parallel flight simulator machine

Robotica ◽  
2021 ◽  
pp. 1-26
Author(s):  
Soheil Zarkandi
Keyword(s):  
Power Consumption ◽  
Dynamic Modeling ◽  
Parallel Manipulator ◽  
Degrees Of Freedom ◽  
Pareto Front ◽  
Essential Role ◽  
Power Optimization ◽  
Optimal Designs ◽  
Flight Simulator ◽  
Lagrange Method

Abstract Reducing consumed power of a robotic machine has an essential role in enhancing its energy efficiency and must be considered during its design process. This paper deals with dynamic modeling and power optimization of a four-degrees-of-freedom flight simulator machine. Simulator cabin of the machine has yaw, pitch, roll and heave motions produced by a 4RPSP+PS parallel manipulator (PM). Using the Euler–Lagrange method, a closed-form dynamic equation is derived for the 4RPSP+PS PM, and its power consumption is computed on the entire workspace. Then, a newly introduced optimization algorithm called multiobjective golden eagle optimizer is utilized to establish a Pareto front of optimal designs of the manipulator having a relatively larger workspace and lower power consumption. The results are verified through numerical examples.

Duplicating Contingent Claims by the Lagrange Method

1999 ◽  
Vol 4 (3) ◽  
pp. 277-284
Author(s):  
Gregory Chow
Keyword(s):  
Contingent Claims ◽  
Lagrange Method
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