#### Overview of this book

Golang is one of the fastest growing programming languages in the software industry. Its speed, simplicity, and reliability make it the perfect choice for building robust applications. This brings the need to have a solid foundation in data structures and algorithms with Go so as to build scalable applications. Complete with hands-on tutorials, this book will guide you in using the best data structures and algorithms for problem solving. The book begins with an introduction to Go data structures and algorithms. You'll learn how to store data using linked lists, arrays, stacks, and queues. Moving ahead, you'll discover how to implement sorting and searching algorithms, followed by binary search trees. This book will also help you improve the performance of your applications by stringing data types and implementing hash structures in algorithm design. Finally, you'll be able to apply traditional data structures to solve real-world problems. By the end of the book, you'll have become adept at implementing classic data structures and algorithms in Go, propelling you to become a confident Go programmer.
Preface
Free Chapter
Section 1: Introduction to Data Structures and Algorithms and the Go Language
Data Structures and Algorithms
Getting Started with Go for Data Structures and Algorithms
Section 2: Basic Data Structures and Algorithms using Go
Linear Data Structures
Non-Linear Data Structures
Homogeneous Data Structures
Heterogeneous Data Structures
Dynamic Data Structures
Classic Algorithms
Section 3: Advanced Data Structures and Algorithms using Go
Network and Sparse Matrix Representation
Memory Management
Next Steps
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# Homogeneous Data Structures

Homogeneous data structures contain similar types of data, such as integers or double values. Homogeneous data structures are used in matrices, as well as tensor and vector mathematics. Tensors are mathematical structures for scalars and vectors. A first-rank tensor is a vector. A vector consists of a row or a column. A tensor with zero rank is a scalar. A matrix is a two-dimensional cluster of numbers. They are all used in scientific analysis.

Tensors are used in material science. They are used in mathematics, physics, mechanics, electrodynamics, and general relativity. Machine learning solutions utilize a tensor data structure. A tensor has properties such as position, shape, and a static size.

This chapter covers the following homogeneous data structures:

• Two-dimensional arrays
• Multi-dimensional arrays

The following scenarios are shown to demonstrate...