Platform data mining is an important branch of data analysis. Traditional methods such as clustering have achieved satisfactory performance. To overcome the shortcomings of traditional algorithms in mathematical optim...
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ISBN:
(数字)9798331523923
ISBN:
(纸本)9798331523930
Platform data mining is an important branch of data analysis. Traditional methods such as clustering have achieved satisfactory performance. To overcome the shortcomings of traditional algorithms in mathematical optimization, this study proposes a novel model based on the sequential quadratic programming algorithm. At the algorithm level, this study first analyses the characteristics of cloud platform data and its requirements for mining efficiency. At the same time, to solve these problems, this study proposed a new data analysis framework that combines sparse factor analysis and embedded database subspace detection. The designed model optimizes attribute dimension selection and dense region extraction to the greatest extent through distribution analysis and feature correlation evaluation. At the same time, the Bayesian network node expansion algorithm is used to model the association of discrete data, and then a cascade data generation method is designed based on this model. Finally, the Bayesian network parameters are optimized by the sequential quadratic programming algorithm, and the approximate value of the Hessian matrix is efficiently solved by the BFGS algorithm, thereby improving the accuracy of the data mining algorithm. The experimental part uses the cloud platform data-set as the target data-set and verifies the stability of the proposed algorithm.
Path planning for space vehicles is still a challenging problem although considerable progress has been made over the past *** major difficulties are that most of existing methods only adapt to static environment inst...
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Path planning for space vehicles is still a challenging problem although considerable progress has been made over the past *** major difficulties are that most of existing methods only adapt to static environment instead of dynamic one,and also can not solve the inherent constraints arising from the robot body and the exterior *** address these difficulties,this research aims to provide a feasible trajectory based on quadratic programming（QP） for path planning in three-dimensional space where an autonomous vehicle is requested to pursue a target while avoiding static or dynamic ***,the objective function is derived from the pursuit task which is defined in terms of the relative distance to the target,as well as the angle between the velocity and the position in the relative velocity coordinates（RVCs）.The optimization is in quadratic polynomial form according to QP ***,the avoidance task is modeled with linear constraints in *** other constraints,such as kinematics,dynamics,and sensor range,are ***,simulations with typical multiple obstacles are carried out,including in static and dynamic environments and one of *** results indicate that the optimal trajectories of the autonomous robot in three-dimensional space satisfy the required ***,the QP model proposed in this paper not only adapts to dynamic environment with uncertainty,but also can satisfy all kinds of constraints,and it provides an efficient approach to solve the problems of path planning in three-dimensional space.
A quadratic programming model is established to choose the blocks to be blasted in a given period. The length of this period depends on the production planning requirements. During the given period, the blocks' pa...
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A quadratic programming model is established to choose the blocks to be blasted in a given period. The length of this period depends on the production planning requirements. During the given period, the blocks' parameters are available from the geological database of the mine. The objective is to minimize the deviation of the average ore grade of blasted blocks from the standard ore grade required by the mill. Transportation ability constraint. production quantity demand constraint. minimum safety bench constraint. block size constraint and block, bench precedence constraints are considered in forming the programming model. This model has more practical objective function and reasonable constraints compared with the existing model for this kind of problems.
Separate-and-conquer type rule induction algorithms such as Ripper, solve a K>2 class problem by converting it into a sequence of K - 1 two-class problems. As a usual heuristic, the classes are fed into the algorit...
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Separate-and-conquer type rule induction algorithms such as Ripper, solve a K>2 class problem by converting it into a sequence of K - 1 two-class problems. As a usual heuristic, the classes are fed into the algorithm in the order of increasing prior probabilities. Although the heuristic works well in practice, there is much room for improvement. In this paper, we propose a novel approach to improve this heuristic. The approach transforms the ordering search problem into a quadratic optimization problem and uses the solution of the optimization problem to extract the optimal ordering. We compared new Ripper (guided by the ordering found with our approach) with original Ripper (guided by the heuristic ordering) on 27 datasets. Simulation results show that our approach produces rulesets that are significantly better than those produced by the original Ripper. (C) 2015 Elsevier B.V. All rights reserved.
In the design of two-dimensional weighted Chebyshev FIR filters, the solution is generally not unique. This is due to the two-dimensional nature of the approximating functions, which implies that the Haar condition ma...
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In the design of two-dimensional weighted Chebyshev FIR filters, the solution is generally not unique. This is due to the two-dimensional nature of the approximating functions, which implies that the Haar condition may not be satisfied. However, for a design on a discrete frequency domain, the Haar condition might be fulfilled. The question of uniqueness is, however, rather extensive to investigate. It is therefore desirable to define some simple additional constraints to the Chebyshev design in order to obtain a unique solution. The weighted Chebyshev solution with minimum Euclidean filter weight norm is always unique, and represents a sensible additional constraint since it implies minimum white noise amplification and less sensitivity to model imperfections for some applications. A quadratic programming formulation is given for this purpose, and it is shown that the formulation can always be made consistent with the unique minimum norm Chebyshev solution. The quadratic formulation is well-conditioned since it consists of a strictly convex objective function and linear constraints, and since it always has a feasible solution. In a fixed, broadband, delay-and-sum beamformer implementation, an important consideration is the robustness of the beamformer response with respect to model imperfections such as inaccuracies in sensor positions and/or sensor frequency response. The beamformer will be robust with respect to such model imperfections if the Euclidean filter weight norm is kept low. When specifying the frequency and angular intervals for a linear and equispaced, broadband delay-and-sum beamformer for the far-field, only a cut wedge region of the corresponding two-dimensional FIR filter frequency plane is specified. With conventional Chebyshev approximation methods in the design of such beamformers, the filter weight values may become excessively large. This anomaly is due to the large, unspecified regions of the corresponding two-dimensional FIR filter frequenc
The authors present a new method to compute solutions to the general multiblock l(1) control problem. The method is based on solving a standard H-2 problem and a finite-dimensional semidefinite quadratic programming p...
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The authors present a new method to compute solutions to the general multiblock l(1) control problem. The method is based on solving a standard H-2 problem and a finite-dimensional semidefinite quadratic programming problem of appropriate dimension. The new method has most of the properties that separately characterize many existing approaches. In particular, as the dimension of the quadratic programming problem increases, this method provides converging upper and lower bounds on the optimal it norm and, for well posed multiblock problems, ensures the convergence in norm of the suboptimal solutions to an optimal l(1) solution. The new method does not require the computation of the interpolation conditions, and it allows the direct computation of the suboptimal controller.
We present a fully polynomial time approximation scheme (FPTAS) for minimizing an objective (a(T)x+gamma)(b(T)x+delta) under linear constraints Ax R+ find an s - t path P that minimizes a( P) b( P), the product of it...
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We present a fully polynomial time approximation scheme (FPTAS) for minimizing an objective (a(T)x+gamma)(b(T)x+delta) under linear constraints Ax <= d. Examples of such problems are combinatorial minimum weight product problems such as the following: given a graph G = ( V, E) and two edge weights a, b : E -> R+ find an s - t path P that minimizes a( P) b( P), the product of its edge weights relative to a and b.
Identifying a subset of features that preserves classification accuracy is a problem of growing importance, because of the increasing size and dimensionality of real-world data sets. We propose a new feature selection...
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Identifying a subset of features that preserves classification accuracy is a problem of growing importance, because of the increasing size and dimensionality of real-world data sets. We propose a new feature selection method, named quadratic programming Feature Selection (QPFS), that reduces the task to a quadratic optimization problem. In order to limit the computational complexity of solving the optimization problem, QPFS uses the Nystrom method for approximate matrix diagonalization. QPFS is thus capable of dealing with very large data sets, for which the use of other methods is computationally expensive. In experiments with small and medium data sets, the QPFS method leads to classification accuracy similar to that of other successful techniques. For large data sets, QPFS is superior in terms of computational efficiency.
In this paper, a quadratic programming (QP) model based on a parametric variational principle is proposed for elastic-plastic (EP) finite element analysis of metal forming processes. The contact problem with friction ...
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In this paper, a quadratic programming (QP) model based on a parametric variational principle is proposed for elastic-plastic (EP) finite element analysis of metal forming processes. The contact problem with friction between blank and tools is treated in the same way as in plastic analysis. The penalty factors, which are normally introduced into the algorithm for contact analysis, have a direct influence on accuracy of solution. There is no available rule for choosing a reasonable value of these factors for simulation of metal forming, and they are therefore cancelled through a special technique so that the numerical results can be of high accuracy. The algorithms for contact analysis and plastic analysis are established in one frame and consistent with each other. Compared with the conventional EP FEM, the newly developed method requires no tedious iterative procedures, and has no convergence problems. To apply this method easily to simulation of metal forming, detailed forms of some key matrices or vectors for 2D FEM and 3D FEM are presented, and a parametric loading algorithm for the QP model is developed, which is suitable for QP problem with free variables, and can decrease memory cost by avoiding the introduction of additional slack variables and improve the solution efficiency to some extent. Finally the proposed QP model is validated by two examples, analysis of V-notched tension test and analysis of the drawing of a square box-one of the benchmarks proposed at NUMISHEET93. It can be seen that the accuracy of solution of the new EP FEM based on QP is better than that of the conventional EP FEM based on iteration. To make the new EP FEM more applicable to metal forming industries, It is necessary to develop a more efficient QP algorithm that is suitable for large-scale problems. (C) 2002 Elsevier Science B.V. All rights reserved.
Raw material fed into a processing or refining plant is required to be uniform in composition for several reasons. when the mined ore is highly variable in quality the only way to ensure consistency is to homogenize t...
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Raw material fed into a processing or refining plant is required to be uniform in composition for several reasons. when the mined ore is highly variable in quality the only way to ensure consistency is to homogenize the ore prior to the process. The problem is more complicated in the case of multiple ore sources. The idea behind the research is that theoretical blending ratios can serve not only to meet predefined specific criteria but also to reduce grade fluctuations of variables under consideration. in this paper, the problem is formulated as a quadratic programming problem, whose objective function is in quadratic form and constraints are linear. A case study was conducted on a data set from an iron orebody to demonstrate the technique. The objective was to minimize the blend variability in terms of each variable (in this study, iron, silica, alumina and lime) grade of ores extracted in three different production faces. The stockpile capacity, lower and upper limits of each variable satisfying operational requirements and non-negativity were constrained to the model. A modified simplex method developed by Wolfe was used for solving the blending and homogenization problem. The promising results can be used as a part of the stacking and reclaiming design.
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