About this course: This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. ## Union−Find We illustrate our basic approach to developing and analyzing algorithms by considering the dynamic connectivity problem. We introduce the union−find data type and consider several implementations (quick find, quick union, weighted quick union, and weighted quick union with path compression). Finally, we apply the union−find data type to the percolation problem from physical chemistry. ## Analysis of Algorithms The basis of our approach for analyzing the performance of algorithms is the scientific method. We begin by performing computational experiments to measure the running times of our programs. W

The design of data structures and algorithms is a book. Explore The design of data structures and algorithms through unique data from The British Library.

The book Data structures and algorithm analysis in C was written by Mark Allen Weiss and published in 1997 by Addison-Wesley. It has an ISBN of 0201498405/0321189957 and is in the eng language. The book is about C♯ (Computer program language), Computer algorithms, Data structures (Computer science) and has a BNB ID of GB9729624.

This dataset contains a collection of scholarly articles focused on the implementation of active learning techniques in data structures courses, with a particular emphasis on Java programming and its application in enhancing student learning in STEM (Science, Technology, Engineering, and Mathematics) disciplines. This collection provides a comprehensive view of various teaching strategies that promote deeper and more meaningful learning through active methods. Each included article has been selected for its relevance, accessibility (Open Access), and contribution to educational practice in programming and data structures.

Keywords: Active learning, data structures, Java programming, STEM, education, teaching strategies, student engagement.

This dataset provides a solid foundation for research and implementation of active learning techniques in data structures and programming courses, benefiting educators and students in the STEM field.

Dataset Contents:

Learning more about active learning Author: Graeme Stemp-Morlock DOI: 10.1145/1498765.1498771 Publication Date: April 1, 2009 Abstract: Discusses how active learning algorithms can reduce label complexity compared to passive methods.

A Compendium of Rationales and Techniques for Active Learning Author: C. Reiness DOI: 10.1187/CBE.20-08-0177 Publication Date: October 1, 2020 Abstract: Provides a collection of strategies for promoting active learning.

Defining Active Learning: A Restricted Systemic Review Authors: Peter Doolittle, Krista Wojdak, Amanda Walters DOI: 10.20343/teachlearninqu.11.25 Publication Date: September 22, 2023 Abstract: Defines active learning as a student-centered approach to knowledge construction focusing on higher-order thinking.

The Curious Construct of Active Learning Authors: D. Lombardi, T. Shipley DOI: 10.1177/1529100620973974 Publication Date: April 1, 2021 Abstract: Discusses the different interpretations of active learning in STEM domains.

Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography Authors: Xuefeng Du, Haohan Wang, Zhenxi Zhu, Xiangrui Zeng, Yi-Wei Chang, Jing Zhang, E. Xing, Min Xu DOI: 10.1093/bioinformatics/btab123 Publication Date: February 23, 2021 Abstract: Proposes a hybrid active learning framework to reduce labeling burden in cryo-ET tasks.

Question Paper Solutions of chapter Searching of Data Structure and Algorithm, 5th Semester , Electrical Engineering

The author Mark A. Weiss has written the book Data structures and algorithm analysis in C++ which was published in 2006 by Addison-Wesley. The book has an ISBN of 0321397339 and is in the English language. The book is about C♯ (Computer program language), Computer algorithms, Data structures (Computer science).

The attached Excel spreadsheet is a codebook for our quantitative data analysis.

Question Paper Solutions of chapter Arrays of Data Structure and Algorithm, 3rd Semester , Bachelor of Computer Application 2020-2021

Simplification algorithms comparison dataset and codes

The book Data structures, algorithms, and applications in Java was written by Sartaj Sahni and published in 2000 by Mc Graw-Hill. It has an ISBN of 0071169008/007109217X and is in the eng language. The book is about Application software-Development, Java (Computer program language), Computer algorithms, Data structures (Computer science) and has a BNB ID of GBA065126.

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

The data files ***.dat provide created configurations in NASTRAN bulk data format.
CHEXA elements were created when the density was more than 0.5.

Question Paper Solutions of chapter Sorting and Hashing of Data Structure and Algorithms, 3rd Semester , Electronics and Communication Engineering

This archive represents the artifact submission for SAS 2021 research paper of the same title. It includes the compressed docker image data_abstraction_benchmarks.tar. The README is reported below # About This docker image contains benchmarks, installation tools and analysis tools for the verification of assertions in programs with arrays. The paper Data Abstraction: A General Framework to Handle Program Verification of Data Structures accepted to SAS2021 uses this tool to generate its benchmark table. # Running the image - Load the image using docker load -i data_abstraction_benchmarks.tar - Run the docker image interactively using : docker run --name="array-benchmarks" -it jbraine/data_abstraction_benchmarks /bin/bash - Source .profile using within the container : source .profile Note : to retrieve files from within the docker container, you may use docker cp from outside the docker image. An example is docker cp array-benchmarks:Tools/array-benchmarks/README.md /tmp/README.md . This only works if you used --name="array-benchmarks" in the docker run command. # Finding the tools Within the docker container, do : cd Tools If you type ls here, you should get 4 folders: - array-benchmarks - DataAbstraction - hornconverter - CellMorphing hornconverter (respectively CellMorphing) is the converter (respectively abstraction tool) from SAS16 Gonnord and Monniaux paper. These are supplied as we use hornconverter as front-end and we compare ourselves with the CellMorphing abstraction. The DataAbstraction tool and the array-benchmarks are our contributions. ## The array-benchmarks The latest stable result build is available in the Latest folder. They currently correspond to the benchmarks used to generate the experiment table of the SAS2021 paper. The README.md within the array-benchmarks folder contains the necessary information to understand the computed results, rebuild them and extend them. ## The DataAbstraction tool Our DataAbstraction tool is contained within the DataAbstraction folder and the current build implements Algorithm 7, page 16 of the SAS2021 paper. The README.md within that folder contains the information on how the source code is build, ran, modified and constructed. It links the algorithms of the paper to specific functions of the source code.

The causal set approach to quantum gravity has gained traction over the past three decades, but numerical experiments involving causal sets have been limited to relatively small scales. The software suite presented here provides a new framework for the generation and study of causal sets. Its efficiency surpasses previous implementations by several orders of magnitude. We highlight several important features of the code, including the compact data structures, the O(N^2) causal set generation process, and several implementations of the O(N^3) algorithm to compute the Benincasa-Dowker action of compact regions of spacetime. We show that by tailoring the data structures and algorithms to take advantage of low-level CPU and GPU architecture designs, we are able to increase the efficiency and reduce the amount of required memory significantly. The presented algorithms and their implementations rely on methods that use CUDA, OpenMP, x86 Assembly, SSE/AVX, Pthreads, and MPI. We also analyze the scaling of the algorithms’ running times with respect to the problem size and available resources, with suggestions on how to modify the code for future hardware architectures.

Python data structures and algorithms : improve the performance and speed of your applications. Explore Python data structures and algorithms : improve the performance and speed of your applications through unique data from The British Library.

The rectangular packing problem has been extensively studied over the years due to its wide application in industry. However, most of the research efforts are devoted to positioning techniques of the rectangles for various problem variants, the efficient implementation of the packing procedure is relatively less studied. In this paper, we propose an efficient constructive algorithm for the rectangular packing problem with rotations. We design a preprocess procedure with four data structures to store the information used for item selection. The gaps on the skyline are categorized into three types according to their associated edges for the placement procedure, during which the item is searched and packed in a descending order of the fitness value. The entire constructive phase takes a time complexity of O(nlogn). For the packing improvement phase, we optimize the packing through random perturbation on the sequence and orientation of the item. Three classes of stochastic problems are generated ranging from small-scale to extra-large-scale, the recorded running time confirms the efficiency of the proposed algorithm. We also test the proposed algorithm on the benchmark problem C21, N13, NT, Babu and CX, the computational results show that it delivers a good performance.

article.pdf

In phylogenomics the analysis of concatenated gene alignments, the so-called supermatrix, is commonly accompanied by the assumption of partition models. Under such models each gene, or more generally partition, is allowed to evolve under its own evolutionary model. Though partition models provide a more comprehensive analysis of supermatrices, missing data may hamper the tree search algorithms due to the existence of phylogenetic (partial) terraces. Here we introduce the phylogenetic terrace aware (PTA) data structure for the efficient analysis under partition models. In the presence of missing data PTA exploits (partial) terraces and induced partition trees to save computation time. We show that an implementation of PTA in IQ-TREE leads to a substantial speedup of up to 4.5 and 8 times compared with the standard IQ-TREE and RAxML implementations, respectively. PTA is generally applicable to all types of partition models and common topological rearrangements thus can be employed by all phylogenomic inference software.