Data Structures
Constant time algorithms have a runtime that does not depend on the
input size. Linear time algorithms have a runtime proportional to the
input size, and quadratic time algorithms have a runtime proportional
to the square of the input size. The binary search algorithm is an
example of an algorithm with logarithmic time complexity.\n\nThe text
then moves on to discuss arrays, which are a fundamental building
block for most data structures. Arrays are fixed-length containers with
indexable elements, usually ranging from 0 to n-1. They are given as
contiguous chunks of memory and are used to store temporary objects,
act as buffers, lookup tables, or as a workaround in a programming
language that allows only one return value. Arrays can be searched, but
it may take up to linear time to traverse all elements. Static arrays
cannot grow or shrink in size, while dynamic arrays can. \n\nThe text
concludes by explaining the basic structure of an array, the common
operations that can be performed on them, and some complexity
analysis. It also provides an example of implementing a dynamic array
using static arrays.
I am explaining how to create a dynamic array using a stack with an
initial capacity of 90. As elements are added, they are added to the
underlying static array, keeping track of the number of elements added.
If an element exceeds the capacity of the internal static array, the array
size is doubled, and all elements are copied into the new array, along
with the new element. The dynamic array source code is provided,
which includes constructors, methods to get and set the size of the
array, to clear the array, and to add and remove elements. The Add
method resizes the array when the length plus one is greater than or
equal to the capacity. The remove and removeAt methods remove a
Constant time algorithms have a runtime that does not depend on the
input size. Linear time algorithms have a runtime proportional to the
input size, and quadratic time algorithms have a runtime proportional
to the square of the input size. The binary search algorithm is an
example of an algorithm with logarithmic time complexity.\n\nThe text
then moves on to discuss arrays, which are a fundamental building
block for most data structures. Arrays are fixed-length containers with
indexable elements, usually ranging from 0 to n-1. They are given as
contiguous chunks of memory and are used to store temporary objects,
act as buffers, lookup tables, or as a workaround in a programming
language that allows only one return value. Arrays can be searched, but
it may take up to linear time to traverse all elements. Static arrays
cannot grow or shrink in size, while dynamic arrays can. \n\nThe text
concludes by explaining the basic structure of an array, the common
operations that can be performed on them, and some complexity
analysis. It also provides an example of implementing a dynamic array
using static arrays.
I am explaining how to create a dynamic array using a stack with an
initial capacity of 90. As elements are added, they are added to the
underlying static array, keeping track of the number of elements added.
If an element exceeds the capacity of the internal static array, the array
size is doubled, and all elements are copied into the new array, along
with the new element. The dynamic array source code is provided,
which includes constructors, methods to get and set the size of the
array, to clear the array, and to add and remove elements. The Add
method resizes the array when the length plus one is greater than or
equal to the capacity. The remove and removeAt methods remove a
