scholarly journals Exclusivity Regularized Machine: A New Ensemble SVM Classifier

2017 ◽  
Author(s):  
Xiaojie Guo ◽  
Xiaobo Wang ◽  
Haibin Ling
Keyword(s):  
Theoretical Analysis ◽  
Lagrange Multiplier ◽  
Linear Complexity ◽  
Optimal Solution ◽  
Svm Classifier ◽  
Global Optimality ◽  
The Arts ◽  
Augmented Lagrange Multiplier ◽  
Training Error

The diversity of base learners is of utmost importance to a good ensemble. This paper defines a novel measurement of diversity, termed as exclusivity. With the designed exclusivity, we further propose an ensemble SVM classifier, namely Exclusivity Regularized Machine (ExRM), to jointly suppress the training error of ensemble and enhance the diversity between bases. Moreover, an Augmented Lagrange Multiplier based algorithm is customized to effectively and efficiently seek the optimal solution of ExRM. Theoretical analysis on convergence, global optimality and linear complexity of the proposed algorithm, as well as experiments are provided to reveal the efficacy of our method and show its superiority over state-of-the-arts in terms of accuracy and efficiency.

An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and its Applications to Mechanical Design

1993 ◽  
Author(s):  
B. K. Kannan ◽  
Steven N. Kramer
Keyword(s):  
Lagrange Multiplier ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Continuous Optimization ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Augmented Lagrange Multiplier

Abstract An algorithm for solving nonlinear optimization problems involving discrete, integer, zero-one and continuous variables is presented. The augmented Lagrange multiplier method combined with Powell’s method and Fletcher & Reeves Conjugate Gradient method are used to solve the optimization problem where penalties are imposed on the constraints for integer / discrete violations. The use of zero-one variables as a tool for conceptual design optimization is also described with an example. Several case studies have been presented to illustrate the practical use of this algorithm. The results obtained are compared with those obtained by the Branch and Bound algorithm. Also, a comparison is made between the use of Powell’s method (zeroth order) and the Conjugate Gradient method (first order) in the solution of these mixed variable optimization problems.

Research on Second Distribution of Real Value Detectors Based on Heuristic Movement

2014 ◽  
Vol 1049-1050 ◽  
pp. 1506-1513
Author(s):  
Tian Bo Wang ◽  
Feng Bin Zhang ◽  
Chun He Xia
Keyword(s):  
Theoretical Analysis ◽  
Anomaly Detection ◽  
Heuristic Algorithm ◽  
Negative Selection ◽  
Optimal Solution ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Generalization Ability ◽  
Real Value ◽  
Omission Rate

Traditional anomaly detection algorithm has improved to some degree the mechanism of negative selection. However, there still remain many problems such as the randomness of detector generation, incompleteness of self-set and the generalization ability of detectors, which would cause a lot of loopholes in non-self-space. This paper proposes a heuristic algorithm based on the second distribution of real value detectors for the remains of loopholes of the non-self-space in the first distribution. The algorithm proposed can distribute real value detectors through omission data based on the methods of partition and movement. A method is then proposed to solve the problem on how to get the optimal solution to the parameters related in the algorithm. Theoretical analysis and experimental results prove the universality and effectiveness of the method. It is found that the algorithm can effectively avoid the generation of loopholes and thus reduce the omission rate of detector sets.

Research on Interception Order Modeling of Air Targets

2011 ◽  
Vol 121-126 ◽  
pp. 1739-1743
Author(s):  
Zhe Shuai Zhou ◽  
Hong Bing Fang
Keyword(s):  
Lagrange Multiplier ◽  
Scenario Analysis ◽  
Influence Factors ◽  
Optimal Solution ◽  
Weapon System ◽  
Improved Method ◽  
Order Model ◽  
Operational Capabilities ◽  
Threat Level ◽  
Typical Scenario

In order to evaluate the threat level of air targets objectively, based on comprehensive consideration the influence factors of threat level of air targets and operational capabilities of weapon system, an improved method of TOPSIS is presented, which is used in comparing and ordering of air targets, and the targets interception order model is also proposed, the optimal solution and the index weights are got by Lagrange multiplier method. Combined with the typical scenario, analysis of example simulation is done and the results show that the method and the model are feasible.

scholarly journals Prediction of drilling leakage locations based on optimized neural networks and the standard random forest method

2021 ◽  
Vol 76 ◽  
pp. 24
Author(s):  
Junlin Su ◽  
Yang Zhao ◽  
Tao He ◽  
Pingya Luo
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Back Propagation ◽  
Optimal Solution ◽  
Southwestern China ◽  
Time Model ◽  
Actual Application ◽  
Network Training ◽  
Training Error ◽  
Parameter Values

Circulation loss is one of the most serious and complex hindrances for normal and safe drilling operations. Detecting the layer at which the circulation loss has occurred is important for formulating technical measures related to leakage prevention and plugging and reducing the wastage because of circulation loss as much as possible. Unfortunately, because of the lack of a general method for predicting the potential location of circulation loss during drilling, most current procedures depend on the plugging test. Therefore, the aim of this study was to use an Artificial Intelligence (AI)-based method to screen and process the historical data of 240 wells and 1029 original well loss cases in a localized area of southwestern China and to perform data mining. Using comparative analysis involving the Genetic Algorithm-Back Propagation (GA-BP) neural network and random forest optimization algorithms, we proposed an efficient real-time model for predicting leakage layer locations. For this purpose, data processing and correlation analysis were first performed using existing data to improve the effects of data mining. The well history data was then divided into training and testing sets in a 3:1 ratio. The parameter values of the BP were then corrected as per the network training error, resulting in the final output of a prediction value with a globally optimal solution. The standard random forest model is a particularly capable model that can deal with high-dimensional data without feature selection. To evaluate and confirm the generated model, the model is applied to eight oil wells in a well site in southwestern China. Empirical results demonstrate that the proposed method can satisfy the requirements of actual application to drilling and plugging operations and is able to accurately predict the locations of leakage layers.

scholarly journals Non-convex nested Benders decomposition

2022 ◽  
Author(s):  
Christian Füllner ◽  
Steffen Rebennack
Keyword(s):  
Benders Decomposition ◽  
Unit Commitment ◽  
Piecewise Linear ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Global Optimality ◽  
Value Functions ◽  
State Variables ◽  
Lipschitz Continuous ◽  
Nested Benders Decomposition

AbstractWe propose a new decomposition method to solve multistage non-convex mixed-integer (stochastic) nonlinear programming problems (MINLPs). We call this algorithm non-convex nested Benders decomposition (NC-NBD). NC-NBD is based on solving dynamically improved mixed-integer linear outer approximations of the MINLP, obtained by piecewise linear relaxations of nonlinear functions. Those MILPs are solved to global optimality using an enhancement of nested Benders decomposition, in which regularization, dynamically refined binary approximations of the state variables and Lagrangian cut techniques are combined to generate Lipschitz continuous non-convex approximations of the value functions. Those approximations are then used to decide whether the approximating MILP has to be dynamically refined and in order to compute feasible solutions for the original MINLP. We prove that NC-NBD converges to an $$\varepsilon $$ ε -optimal solution in a finite number of steps. We provide promising computational results for some unit commitment problems of moderate size.

scholarly journals Mixture Augmented Lagrange Multiplier Method for Tensor Recovery and Its Applications

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Huachun Tan ◽  
Bin Cheng ◽  
Jianshuai Feng ◽  
Li Liu ◽  
Wuhong Wang
Keyword(s):  
Lagrange Multiplier ◽  
Principal Component ◽  
Real Data ◽  
Video Data ◽  
Lagrange Multiplier Method ◽  
Multiplier Method ◽  
Traffic Data ◽  
Augmented Lagrange Multiplier ◽  
Tensor Recovery ◽  
Augmented Lagrange Multiplier Method

The problem of data recovery in multiway arrays (i.e., tensors) arises in many fields such as computer vision, image processing, and traffic data analysis. In this paper, we propose a scalable and fast algorithm for recovering a low-n-rank tensor with an unknown fraction of its entries being arbitrarily corrupted. In the new algorithm, the tensor recovery problem is formulated as a mixture convex multilinear Robust Principal Component Analysis (RPCA) optimization problem by minimizing a sum of the nuclear norm and theℓ1-norm. The problem is well structured in both the objective function and constraints. We apply augmented Lagrange multiplier method which can make use of the good structure for efficiently solving this problem. In the experiments, the algorithm is compared with the state-of-art algorithm both on synthetic data and real data including traffic data, image data, and video data.

The Improvement of Optimality Test over Possible Reaction Set in Bilevel Linear Optimization with Ambiguous Objective Function of the Follower

2015 ◽  
Vol 19 (5) ◽  
pp. 645-654 ◽  
Author(s):  
Puchit Sariddichainunta ◽  
◽  
Masahiro Inuiguchi
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Linear Optimization ◽  
Convex Polytope ◽  
Optimal Solution ◽  
Global Optimality ◽  
Basic Solution ◽  
Bilevel Linear Programming ◽  
Coefficient Vector ◽  
Optimality Test

Verifying a rational response is the most crucial step in searching for an optimal solution in bilevel linear programming. Such verification is even difficult in a model with ambiguous objective function of the follower who reacts rationally to a leader’s decision. In our model, we assume that the ambiguous coefficient vector of follower lies in a convex polytope and we formulate bilevel linear programming with the ambiguous objective function of the follower as a special three-level programming problem. We use thek-th best method that sequentially enumerates a solution and examine whether it is the best of all possible reactions. The optimality test process over possible reactions in lower-level problems usually encounters degenerate bases that become obstacles to verifying the optimality of an enumerated solution efficiently. To accelerate optimality verification, we propose search strategies and the evaluation of basic possible reactions adjacent to a degenerate basic solution. We introduce these methods in both local and global optimality testing, confirming the effectiveness of our proposed methods in numerical experiments.

The Augmented Lagrange Multiplier for robust visual tracking with sparse representation

Optik ◽  
2015 ◽  
Vol 126 (9-10) ◽  
pp. 937-941 ◽  
Author(s):  
Ying Shi ◽  
Yan Zhao ◽  
Nianmao Deng ◽  
Kui Yang
Keyword(s):  
Sparse Representation ◽  
Lagrange Multiplier ◽  
Visual Tracking ◽  
Augmented Lagrange Multiplier
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