JPROFILE102: A system for experimental analysis of algorithms

2012 ◽  
Author(s):  
Tanin Krajangthong ◽  
Somchai Prasitjutrakul
Keyword(s):  
Experimental Analysis ◽  
Analysis Of Algorithms ◽  
Experimental Analysis Of Algorithms

scholarly journals Bilinear Assignment Problem: Large Neighborhoods and Experimental Analysis of Algorithms

2020 ◽  
Vol 32 (3) ◽  
pp. 730-746
Author(s):  
Vladyslav Sokol ◽  
Ante Ćustić ◽  
Abraham P. Punnen ◽  
Binay Bhattacharya
Keyword(s):  
Experimental Analysis ◽  
Assignment Problem ◽  
Heuristic Algorithms ◽  
Analysis Of Algorithms ◽  
Quadratic Assignment Problem ◽  
Point Of View ◽  
Neighborhood Search ◽  
Quadratic Assignment ◽  
Neighborhood Structures ◽  
Experimental Analysis Of Algorithms

The bilinear assignment problem (BAP) is a generalization of the well-known quadratic assignment problem. In this paper, we study the problem from the computational analysis point of view. Several classes of neighborhood structures are introduced for the problem along with some theoretical analysis. These neighborhoods are then explored within a local search and variable neighborhood search frameworks with multistart to generate robust heuristic algorithms. In addition, we present several very fast construction heuristics. Our systematic experimental analysis disclosed some interesting properties of the BAP, different from those of comparable models. We have also introduced benchmark test instances that can be used for future experiments on exact and heuristic algorithms for the problem.

scholarly journals The Design and Experimental Analysis of Algorithms for Temporal Reasoning

1996 ◽  
Vol 4 ◽  
pp. 1-18 ◽  
Author(s):  
P. Van Beek ◽  
D. W. Manchak
Keyword(s):  
Analysis Of Algorithms ◽  
Temporal Reasoning ◽  
Temporal Information ◽  
Search Problem ◽  
Planning And Scheduling ◽  
Backtracking Search ◽  
The Core ◽  
Reasoning System ◽  
Space Requirements ◽  
Experimental Analysis Of Algorithms

Many applications -- from planning and scheduling to problems in molecular biology -- rely heavily on a temporal reasoning component. In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen's influential interval-based framework for representing temporal information. At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information. Two important algorithms for these tasks are a path consistency algorithm and a backtracking algorithm. For the path consistency algorithm, we develop techniques that can result in up to a ten-fold speedup over an already highly optimized implementation. For the backtracking algorithm, we develop variable and value ordering heuristics that are shown empirically to dramatically improve the performance of the algorithm. As well, we show that a previously suggested reformulation of the backtracking search problem can reduce the time and space requirements of the backtracking search. Taken together, the techniques we develop allow a temporal reasoning component to solve problems that are of practical size.

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