Competitive analysis of algorithms

1998 ◽  
pp. 1-12 ◽  
Author(s):  
Amos Fiat ◽  
Gerhard J. Woeginger
Keyword(s):  
Competitive Analysis ◽  
Analysis Of Algorithms

scholarly journals A competitive analysis of algorithms for searching unknown scenes

1993 ◽  
Vol 3 (3) ◽  
pp. 139-155 ◽  
Author(s):  
Bala Kalyanasundaram ◽  
Kirk Pruhs
Keyword(s):  
Competitive Analysis ◽  
Analysis Of Algorithms

Competitive Analysis for the 3-Slope Ski-Rental Problem with the Discount Rate

2016 ◽  
Vol E99.A (6) ◽  
pp. 1075-1083 ◽  
Author(s):  
Hiroshi FUJIWARA ◽  
Shunsuke SATOU ◽  
Toshihiro FUJITO
Keyword(s):  
Discount Rate ◽  
Competitive Analysis

Comparison and Analysis of Algorithms Used for Detecting Slums in Satellite Images

2019 ◽  
Author(s):  
Pallavi Saindane ◽  
Gayatri Ganapathy ◽  
Neha Prabhavalkar ◽  
Nilesh Bhatia ◽  
Aishwarya Vaidya
Keyword(s):  
Satellite Images ◽  
Analysis Of Algorithms ◽  
Comparison And Analysis

Computer algorithms

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Data Structures ◽  
Analysis Of Algorithms ◽  
Shortest Paths ◽  
Minimum Cut ◽  
Network Data ◽  
Computer Algorithms ◽  
Breadth First Search ◽  
Augmenting Path ◽  
Basic Concepts ◽  
Maximum Flows

This chapter introduces some of the fundamental concepts of numerical network calculations. The chapter starts with a discussion of basic concepts of computational complexity and data structures for storing network data, then progresses to the description and analysis of algorithms for a range of network calculations: breadth-first search and its use for calculating shortest paths, shortest distances, components, closeness, and betweenness; Dijkstra's algorithm for shortest paths and distances on weighted networks; and the augmenting path algorithm for calculating maximum flows, minimum cut sets, and independent paths in networks.

Tiered Sampling

2021 ◽  
Vol 15 (5) ◽  
pp. 1-52
Author(s):  
Lorenzo De Stefani ◽  
Erisa Terolli ◽  
Eli Upfal
Keyword(s):  
Large Scale ◽  
Analysis Of Algorithms ◽  
Base Layer ◽  
Single Edge ◽  
Real World Data ◽  
High Quality ◽  
Large Graphs ◽  
Massive Graphs ◽  
Variance Estimate ◽  
Low Probability

We introduce Tiered Sampling , a novel technique for estimating the count of sparse motifs in massive graphs whose edges are observed in a stream. Our technique requires only a single pass on the data and uses a memory of fixed size M , which can be magnitudes smaller than the number of edges. Our methods address the challenging task of counting sparse motifs—sub-graph patterns—that have a low probability of appearing in a sample of M edges in the graph, which is the maximum amount of data available to the algorithms in each step. To obtain an unbiased and low variance estimate of the count, we partition the available memory into tiers (layers) of reservoir samples. While the base layer is a standard reservoir sample of edges, other layers are reservoir samples of sub-structures of the desired motif. By storing more frequent sub-structures of the motif, we increase the probability of detecting an occurrence of the sparse motif we are counting, thus decreasing the variance and error of the estimate. While we focus on the designing and analysis of algorithms for counting 4-cliques, we present a method which allows generalizing Tiered Sampling to obtain high-quality estimates for the number of occurrence of any sub-graph of interest, while reducing the analysis effort due to specific properties of the pattern of interest. We present a complete analytical analysis and extensive experimental evaluation of our proposed method using both synthetic and real-world data. Our results demonstrate the advantage of our method in obtaining high-quality approximations for the number of 4 and 5-cliques for large graphs using a very limited amount of memory, significantly outperforming the single edge sample approach for counting sparse motifs in large scale graphs.

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