scholarly journals Efficient CTL Verification via Horn Constraints Solving

2016 ◽  
Vol 219 ◽  
pp. 1-14 ◽  
Author(s):  
Tewodros A. Beyene ◽  
Corneliu Popeea ◽  
Andrey Rybalchenko
Keyword(s):  
Constraints Solving

scholarly journals A Comparative Study of Redundant Constraints Identification Methods in Linear Programming Problems

2010 ◽  
Vol 2010 ◽  
pp. 1-16 ◽  
Author(s):  
Paulraj S. ◽  
Sumathi P.
Keyword(s):  
Linear Programming ◽  
Large Scale ◽  
Optimization Problems ◽  
Inequality Constraints ◽  
Computational Effort ◽  
Linear Functions ◽  
Redundant Constraints ◽  
Computer Scientists ◽  
Constraints Solving ◽  
Linear Programming Problems

The objective function and the constraints can be formulated as linear functions of independent variables in most of the real-world optimization problems. Linear Programming (LP) is the process of optimizing a linear function subject to a finite number of linear equality and inequality constraints. Solving linear programming problems efficiently has always been a fascinating pursuit for computer scientists and mathematicians. The computational complexity of any linear programming problem depends on the number of constraints and variables of the LP problem. Quite often large-scale LP problems may contain many constraints which are redundant or cause infeasibility on account of inefficient formulation or some errors in data input. The presence of redundant constraints does not alter the optimal solutions(s). Nevertheless, they may consume extra computational effort. Many researchers have proposed different approaches for identifying the redundant constraints in linear programming problems. This paper compares five of such methods and discusses the efficiency of each method by solving various size LP problems and netlib problems. The algorithms of each method are coded by using a computer programming language C. The computational results are presented and analyzed in this paper.

scholarly journals Conditional Preference Network with Constraints and Uncertainty

2019 ◽  
Author(s):  
Sultan Ahmed
Keyword(s):  
Graphical Model ◽  
Ceteris Paribus ◽  
Hard Constraints ◽  
Dominance Testing ◽  
Conditional Preference ◽  
Constraints Solving ◽  
Habitual Behavior ◽  
Future Work

In multi-attribute preference-based reasoning, the CP-net is a graphical model to represent user's conditional ceteris paribus (all else being equal) preference statements. This paper outlines three aspects of the CP-net. First, when a CP-net is involved with a set of hard constraints, solving the Constrained CP-net requires dominance testing which is a very expensive operation. We tackle this problem by extending the CP-net model such that dominance testing is not needed. Second, user's choices involve habitual behavior and genuine decision. The former is represented using preferences, while we introduce the notion of comfort to represent the latter. Then, we suggest an extension of the CP-net which can represent both preference and comfort. Third, preferences often come with noise and uncertainty. In this regard, we suggest the probabilistic extension of the Tradeoff-enhanced CP-net (TCP-net) model. The necessary semantics and usefulness of the extensions above are described. Finally, we outline some in-progress and future work.

Automated String Constraints Solving for Programs Containing String Manipulation Functions

2017 ◽  
Vol 32 (6) ◽  
pp. 1125-1135 ◽  
Author(s):  
Xu-Zhou Zhang ◽  
Yun-Zhan Gong ◽  
Ya-Wen Wang ◽  
Ying Xing ◽  
Ming-Zhe Zhang
Keyword(s):  
Constraints Solving

Fuel Optimization Using Biologically-Inspired Computational Models

2008 ◽  
Author(s):  
Thamar E. Mora ◽  
Abu B. Sesay ◽  
Jo¨rg Denzinger ◽  
H. Golshan ◽  
G. Poissant ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Computational Models ◽  
State Of The Art ◽  
Optimization Method ◽  
Operating Conditions ◽  
Optimization Approach ◽  
Biologically Inspired ◽  
Natural Gas Pipeline ◽  
Constraints Solving ◽  
Fuel Optimization

This paper presents a method for optimizing the fuel consumption of large and complex natural gas pipeline systems. The optimization method uses a biologically-inspired computational model, namely Particle Swarm Systems. The main objective is to identify the set of operating conditions that minimizes the use of fuel in compressor stations while maintaining the desired throughput and satisfying given system constraints. Solving this fuel optimization problem is non-trivial given the large number of decision variables and constraints in large networks, the nature of the fuel function and the minimum response time imposed by the frequent changes in flow nominations. The experimental evaluation tested on various subnetworks of TransCanada show that the proposed optimization approach meets TransCanada’s time requirements and reliably outperforms the interactive method that is the current state-of-the-art by providing solutions for which the fuel consumption is 12% less than state-of-the-art methods.

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