Validity of Local Search in Multiparametric and Vector Linear Programming

1988 ◽  
Vol 39 (1) ◽  
pp. 83
Author(s):  
R. Hartley
Keyword(s):  
Linear Programming ◽  
Local Search ◽  
Vector Linear Programming

scholarly journals Sentiment Analysis Using Treebank Filtered Preprocess with Relevant Vector Boost Classifier

2021 ◽  
Author(s):  
Rajendiran P.Rajendiran ◽  
P.L.K. Priyadarsini
Keyword(s):  
Linear Programming ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Prediction Accuracy ◽  
Real Life ◽  
Classification Algorithm ◽  
Stock Market Prediction ◽  
Ensemble Technique ◽  
Customer Reviews ◽  
Vector Linear Programming

Abstract The procedure of identifying and classifying opinions in a piece of text to find out whether customer reviews towards a particular product or service are positive, negative, or neutral is termed as sentiment analysis. Stock market prediction is one of the most attractive topics in academic and real-life business. Many data mining techniques about sentiment analysis are suffering from the inaccuracy of prediction. The low classification accuracy has a direct effect on the reliability of stock market indicators. Treebank filtering Data Preprocessing based Ochiai-Barkman Relevance Vector Linear Programming Boost Classification (TFDP-ORVLPBC) technique is used for stock market prediction using sentimental analysis with higher prediction accuracy and lesser classification time for enhancing accuracy of stock market based on product review. Initially, the customer reviews and feedback on services or products are collected from the large database. After that, the collected customer reviews are preprocessed by performing the process such as tokenization, stemming, filtering. In order to achieve sentimental analysis through classifying customer reviews as positive and negative, Ochiai-Barkman Relevance Vector Linear Programming Boost Classification algorithm is used. The Linear Programming Boost Classification algorithm constructs with an empty set of weak classifiers as the Ochiai-Barkman Relevance Vector machine. The customer reviews are classified based on the Ochiai-Barkman similarity coefficient. The ensemble technique combines the weak classification results into strong by minimizing the error. In this way, the classification performance gets improved and the prediction of the stock market is carried out in a more accurate manner. Experimental evaluation is carried out on factors such as prediction accuracy, sensitivity, specificity, and prediction time versus amount of customer reviews.

Global Results Synthesis

2020 ◽  
pp. 165-175
Keyword(s):  
Linear Programming ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Local Search ◽  
Constraint Programming ◽  
Cpu Time ◽  
Exact Approach ◽  
First Results ◽  
Programming Techniques ◽  
Portfolio Design

This chapter provides a global synthesis of the realized results by applying exact and approximate approaches on the portfolio design (PD) problem. The authors introduce an experimental analysis of best approaches based on linear programming and constraint programming techniques, according to the CPU time. Next, a global experiment synthesis of the best approximate approaches based on Simulated Annealing, IDWalk, Tabu Search, GWW, and VNS is realized according to the number of success and the CPU time. First results show that constraint programming with breaking all the detected symmetries is the best as an exact approach, VNS combined with simulated annealing is effective on non-trivial instances of the problem, and simulated annealing is the most effective as a simple local search.

Piece-Mold-Machine Manufacturing Planning

2012 ◽  
pp. 867-879
Author(s):  
O. J. Ibarra-Rojas ◽  
Y. A. Rios-Solis ◽  
O. L. Chacon-Mondragon
Keyword(s):  
Linear Programming ◽  
Local Search ◽  
Integer Linear Programming ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Programming Formulation ◽  
Manufacturing Planning ◽  
Linear Programming Formulation ◽  
Solution Methodology ◽  
Integer Linear Programming Formulation

This chapter studies a manufacturing process of pieces. These pieces are produced with molds which are mounted on machines. The authors describe this process as an optimization problem using an integer linear programming formulation which integrates the most important features of the system, and determines the quantities of pieces to produce, including the allocation of molds to machines. The objective function is to maximize the weighted production since the authors seek to minimize the non-fulfilled demand. First they show that the addressed problem belongs to the NP-hard class. After observing that solving the problem in an exact way is time consuming, they propose a solution methodology based on an Iterated Local Search Algorithm. Through computational experimentation they make conclusions about the difficulty of the decisions determined in this manufacturing planning.

Piece-Mold-Machine Manufacturing Planning

2010 ◽  
pp. 105-117
Author(s):  
O. J. Ibarra-Rojas ◽  
Y. A. Rios-Solis ◽  
O. L. Chacon-Mondragon
Keyword(s):  
Linear Programming ◽  
Local Search ◽  
Integer Linear Programming ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Programming Formulation ◽  
Manufacturing Planning ◽  
Linear Programming Formulation ◽  
Solution Methodology ◽  
Integer Linear Programming Formulation

This chapter studies a manufacturing process of pieces. These pieces are produced with molds which are mounted on machines. The authors describe this process as an optimization problem using an integer linear programming formulation which integrates the most important features of the system, and determines the quantities of pieces to produce, including the allocation of molds to machines. The objective function is to maximize the weighted production since the authors seek to minimize the non-fulfilled demand. First they show that the addressed problem belongs to the NP-hard class. After observing that solving the problem in an exact way is time consuming, they propose a solution methodology based on an Iterated Local Search Algorithm. Through computational experimentation they make conclusions about the difficulty of the decisions determined in this manufacturing planning.

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