scholarly journals A Portfolio Approach to Landscape Plant Production and Marketing

1993 ◽  
Vol 25 (2) ◽  
pp. 13-26 ◽  
Author(s):  
David L. Purcell ◽  
Steven C. Turner ◽  
Jack Houston ◽  
Charles Hall
Keyword(s):  
Quadratic Programming ◽  
Decision Model ◽  
Programming Model ◽  
Portfolio Analysis ◽  
Plant Production ◽  
Empirical Results ◽  
Landscape Plant ◽  
Portfolio Approach ◽  
Income Variability

AbstractThe ornamental horticultural industry continues to be one of the most rapidly expanding sectors in agriculture. This study examined a decision model for landscape plant production based on portfolio analysis. A quadratic programming model was developed to generate an optimal crop portfolio for a selected southeastern nursery. Empirical results indicate opportunities exist for modest diversification to offset income variability in landscape plant production and marketing.

scholarly journals Solution of Quadratic Programming with Interval Variables Using a Two-Level Programming Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Syaripuddin ◽  
Herry Suprajitno ◽  
Fatmawati
Keyword(s):  
Quadratic Programming ◽  
Programming Model ◽  
Programming Models ◽  
Programming Approach ◽  
Optimum Point ◽  
Interval Variables ◽  
Interval Coefficients ◽  
Optimum Solution

Quadratic programming with interval variables is developed from quadratic programming with interval coefficients to obtain optimum solution in interval form, both the optimum point and optimum value. In this paper, a two-level programming approach is used to solve quadratic programming with interval variables. Procedure of two-level programming is transforming the quadratic programming model with interval variables into a pair of classical quadratic programming models, namely, the best optimum and worst optimum problems. The procedure to solve the best and worst optimum problems is also constructed to obtain optimum solution in interval form.

scholarly journals Formulation of Broiler Finishing Rations by Quadratic Programming

1986 ◽  
Vol 18 (1) ◽  
pp. 141-150 ◽  
Author(s):  
Bill R. Miller ◽  
Ronaldo A. Arraes ◽  
Gene M. Pesti
Keyword(s):  
Linear Programming ◽  
Quadratic Programming ◽  
Programming Model ◽  
Nutrition Knowledge ◽  
Information Analysis ◽  
Response Information ◽  
Constraint Set ◽  
Protein Density ◽  
Production Response ◽  
Reducing Energy

AbstractLeast cost feed mix by linear programming (LP) is a standard economic analysis in the poultry industry. A significant body of nutrition knowledge is now contained in the constraint set of industry LP models. This knowledge might be merged into an improved economic model that contains production response information. Analysis using a quadratic programming model indicated that a leading broiler firm could have improved economic efficiency by increasing protein density and reducing energy density of broiler finisher feed. If applicable industry wide, similar savings could be as high as $120 million per year.

A MULTI-CLASS SUPPORT VECTOR MACHINE: THEORY AND MODEL

2013 ◽  
Vol 12 (06) ◽  
pp. 1175-1199 ◽  
Author(s):  
MINGHE SUN
Keyword(s):  
Support Vector Machine ◽  
Quadratic Programming ◽  
Programming Model ◽  
Inner Product ◽  
High Dimensional ◽  
Support Vector ◽  
Test Problems ◽  
Discriminant Functions ◽  
Input Space ◽  
Feature Spaces

A multi-class support vector machine (M-SVM) is developed, its dual is derived, its dual is mapped to high dimensional feature spaces using inner product kernels, and its performance is tested. The M-SVM is formulated as a quadratic programming model. Its dual, also a quadratic programming model, is very elegant and is easier to solve than the primal. The discriminant functions can be directly constructed from the dual solution. By using inner product kernels, the M-SVM can be built and nonlinear discriminant functions can be constructed in high dimensional feature spaces without carrying out the mappings from the input space to the feature spaces. The size of the dual, measured by the number of variables and constraints, is independent of the dimension of the input space and stays the same whether the M-SVM is built in the input space or in a feature space. Compared to other models published in the literature, this M-SVM is equally or more effective. An example is presented to demonstrate the dual formulation and solution in feature spaces. Very good results were obtained on benchmark test problems from the literature.

scholarly journals A Television Recommender System Learning a User’s Time-Aware Watching Patterns Using Quadratic Programming

2018 ◽  
Vol 8 (8) ◽  
pp. 1323 ◽  
Author(s):  
Noo-ri Kim ◽  
Sungtak Oh ◽  
Jee-Hyong Lee
Keyword(s):  
Quadratic Programming ◽  
Optimization Problem ◽  
Programming Model ◽  
Multiple Time ◽  
Computational Power ◽  
Tv Program ◽  
Minimum Number ◽  
Preference Models ◽  
Available Information ◽  
Time Aware

In this paper, a novel television (TV) program recommendation method is proposed by merging multiple preferences. We use channels and genres of programs, which is available information in standalone TVs, as features for the recommendation. The proposed method performs multi-time contextual profiling and constructs multiple-time contextual preference matrices of channels and genres. Since multiple preference models are constructed with different time contexts, there can be conflicts among them. In order to effectively merge the preferences with the minimum number of conflicts, we develop a quadratic programming model. The optimization problem is formulated with a minimum number of constraints so that the optimization process is scalable and fast even in a standalone TV with low computational power. Experiments with a real-world dataset prove that the proposed method is more efficient and accurate than other TV recommendation methods. Our method improves recommendation performance by 5–50% compared to the baselines.

A Novel Quadratic Programming Model for Soft-Input Soft-Output MIMO Detection

2007 ◽  
Vol 14 (12) ◽  
pp. 924-927 ◽  
Author(s):  
Zi Li ◽  
Chengkang Pan ◽  
Yueming Cai ◽  
Youyun Xu
Keyword(s):  
Quadratic Programming ◽  
Programming Model ◽  
Mimo Detection
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