hello-algo/docs-en/chapter_computational_complexity/space_complexity.md

Space Complexity

The space complexity is used to measure the growth trend of memory consumption as the scale of data increases for an algorithm solution. This concept is analogous to time complexity by replacing "runtime" with "memory space".

Algorithmic Correlation Space

The memory space used by algorithms during its execution include the following types.

• Input Space: Used to store the input data for the algorithm.
• Temporary Space: Used to store variables, objects, function contexts, and other data of the algorithm during runtime.
• Output Space: Used to store the output data of the algorithm.

In general, the "Input Space" is excluded from the statistics of space complexity.

The Temporary Space can be further divided into three parts.

• Temporary Data: Used to store various constants, variables, objects, etc., during the the algorithm's execution.
• Stack Frame Space: Used to hold the context data of the called function. The system creates a stack frame at the top of the stack each time a function is called, and the stack frame space is freed when the function returns.
• Instruction Space: Used to hold compiled program instructions, usually ignored in practical statistics.

When analyzing the space complexity of a piece of program, three parts are usually taken into account: Temporary Data, Stack Frame Space and Output Data.

=== "Python"

```python title=""
class Node:
    """Classes""""
    def __init__(self, x: int):
        self.val: int = x               # node value
        self.next: Node | None = None   # reference to the next node

def function() -> int:
    """"Functions"""""
    # Perform certain operations...
    return 0

def algorithm(n) -> int:    # input data
    A = 0                   # temporary data (constant, usually in uppercase)
    b = 0                   # temporary data (variable)
    node = Node(0)          # temporary data (object)
    c = function()          # Stack frame space (call function)
    return A + b + c        # output data
```

=== "C++"

```cpp title=""
/* Structures */
struct Node {
    int val;
    Node *next;
    Node(int x) : val(x), next(nullptr) {}
};

/* Functions */
int func() {
    // Perform certain operations...
    return 0;
}

int algorithm(int n) {          // input data
    const int a = 0;            // temporary data (constant)
    int b = 0;                  // temporary data (variable)
    Node* node = new Node(0);   // temporary data (object)
    int c = func();             // stack frame space (call function)
    return a + b + c;           // output data
}
```

=== "Java"

```java title=""
/* Classes */
class Node {
    int val;
    Node next;
    Node(int x) { val = x; }
}

/* Functions */
int function() {
    // Perform certain operations...
    return 0;
}

int algorithm(int n) {          // input data
    final int a = 0;            // temporary data (constant)
    int b = 0;                  // temporary data (variable)
    Node node = new Node(0);    // temporary data (object)
    int c = function();         // stack frame space (call function)
    return a + b + c;           // output data
}
```

=== "C#"

```csharp title=""
/* Classes */
class Node {
    int val;
    Node next;
    Node(int x) { val = x; }
}

/* Functions */
int Function() {
    // Perform certain operations...
    return 0;
}

int Algorithm(int n) {  // input data
    const int a = 0;    // temporary data (constant)
    int b = 0;          // temporary data (variable)
    Node node = new(0); // temporary data (object)
    int c = Function(); // stack frame space (call function)
    return a + b + c;   // output data
}
```

=== "Go"

```go title=""
/* Structures */
type node struct {
    val  int
    next *node
}

/* Create node structure */
func newNode(val int) *node {
    return &node{val: val}
}

/* Functions */
func function() int {
    // Perform certain operations...
    return 0
}

func algorithm(n int) int { // input data
    const a = 0             // temporary data (constant)
    b := 0                  // temporary storage of data (variable)
    newNode(0)              // temporary data (object)
    c := function()         // stack frame space (call function)
    return a + b + c        // output data
}
```

=== "Swift"

```swift title=""
/* Classes */
class Node {
    var val: Int
    var next: Node?

    init(x: Int) {
        val = x
    }
}

/* Functions */
func function() -> Int {
    // Perform certain operations...
    return 0
}

func algorithm(n: Int) -> Int { // input data
    let a = 0                   // temporary data (constant)
    var b = 0                   // temporary data (variable)
    let node = Node(x: 0)       // temporary data (object)
    let c = function()          // stack frame space (call function)
    return a + b + c            // output data
}
```

=== "JS"

```javascript title=""
/* Classes */
class Node {
    val;
    next;
    constructor(val) {
        this.val = val === undefined ? 0 : val; // node value
        this.next = null;                       // reference to the next node
    }
}

/* Functions */
function constFunc() {
    // Perform certain operations
    return 0;
}

function algorithm(n) {         // input data
    const a = 0;                // temporary data (constant)
    let b = 0;                  // temporary data (variable)
    const node = new Node(0);   // temporary data (object)
    const c = constFunc();      // Stack frame space (calling function)
    return a + b + c;           // output data
}
```

=== "TS"

```typescript title=""
/* Classes */
class Node {
    val: number;
    next: Node | null;
    constructor(val?: number) {
        this.val = val === undefined ? 0 : val; // node value
        this.next = null;                       // reference to the next node
    }
}

/* Functions */
function constFunc(): number {
    // Perform certain operations
    return 0;
}

function algorithm(n: number): number { // input data
    const a = 0;                        // temporary data (constant)
    let b = 0;                          // temporary data (variable)
    const node = new Node(0);           // temporary data (object)
    const c = constFunc();              // Stack frame space (calling function)
    return a + b + c;                   // output data
}
```

=== "Dart"

```dart title=""
/* Classes */
class Node {
  int val;
  Node next;
  Node(this.val, [this.next]);
}

/* Functions */
int function() {
  // Perform certain operations...
  return 0;
}

int algorithm(int n) {  // input data
  const int a = 0;      // temporary data (constant)
  int b = 0;            // temporary data (variable)
  Node node = Node(0);  // temporary data (object)
  int c = function();   // stack frame space (call function)
  return a + b + c;     // output data
}
```

=== "Rust"

```rust title=""
use std::rc::Rc;
use std::cell::RefCell;

/* Structures */
struct Node {
    val: i32,
    next: Option<Rc<RefCell<Node>>>,
}

/* Creating a Node structure */
impl Node {
    fn new(val: i32) -> Self {
        Self { val: val, next: None }
    }
}

/* Functions */
fn function() -> i32 {     
    // Perform certain operations...
    return 0;
}

fn algorithm(n: i32) -> i32 {   // input data
    const a: i32 = 0;           // temporary data (constant)
    let mut b = 0;              // temporary data (variable)
    let node = Node::new(0);    // temporary data (object)
    let c = function();         // stack frame space (call function)
    return a + b + c;           // output data
}
```

=== "C"

```c title=""
/* Functions */
int func() {
    // Perform certain operations...
    return 0;
}

int algorithm(int n) {  // input data
    const int a = 0;    // temporary data (constant)
    int b = 0;          // temporary data (variable)
    int c = func();     // stack frame space (call function)
    return a + b + c;   // output data
}
```

=== "Zig"

```zig title=""

```

Calculation Method

The calculation method for space complexity is pretty similar to time complexity, with the only difference being that the focus shifts from "operation count" to "space usage size".

On top of that, unlike time complexity, we usually only focus on the worst-case space complexity. This is because memory space is a hard requirement, and we have to make sure that there is enough memory space reserved for all possibilities incurred by input data.

Looking at the following code, the "worst" in worst-case space complexity has two layers of meaning.

1. Based on the worst-case input data: when n < 10, the space complexity is O(1); however, when n > 10, the initialized array nums occupies O(n) space; thus the worst-case space complexity is O(n).
2. Based on the peak memory during algorithm execution: for example, the program occupies O(1) space until the last line is executed; when the array nums is initialized, the program occupies O(n) space; thus the worst-case space complexity is O(n).

=== "Python"

```python title=""
def algorithm(n: int):
    a = 0               # O(1)
    b = [0] * 10000     # O(1)
    if n > 10:
        nums = [0] * n  # O(n)
```

=== "C++"

```cpp title=""
void algorithm(int n) {
    int a = 0;               // O(1)
    vector<int> b(10000);    // O(1)
    if (n > 10)
        vector<int> nums(n); // O(n)
}
```

=== "Java"

```java title=""
void algorithm(int n) {
    int a = 0;                   // O(1)
    int[] b = new int[10000];    // O(1)
    if (n > 10)
        int[] nums = new int[n]; // O(n)
}
```

=== "C#"

```csharp title=""
void Algorithm(int n) {
    int a = 0;                   // O(1)
    int[] b = new int[10000];    // O(1)
    if (n > 10) {
        int[] nums = new int[n]; // O(n)
    }
}
```

=== "Go"

```go title=""
func algorithm(n int) {
    a := 0                      // O(1)
    b := make([]int, 10000)     // O(1)
    var nums []int
    if n > 10 {
        nums := make([]int, n)  // O(n)
    }
    fmt.Println(a, b, nums)
}
```

=== "Swift"

```swift title=""
func algorithm(n: Int) {
    let a = 0 // O(1)
    let b = Array(repeating: 0, count: 10000) // O(1)
    if n > 10 {
        let nums = Array(repeating: 0, count: n) // O(n)
    }
}
```

=== "JS"

```javascript title=""
function algorithm(n) {
    const a = 0;                   // O(1)
    const b = new Array(10000);    // O(1)
    if (n > 10) {
        const nums = new Array(n); // O(n)
    }
}
```

=== "TS"

```typescript title=""
function algorithm(n: number): void {
    const a = 0;                   // O(1)
    const b = new Array(10000);    // O(1)
    if (n > 10) {
        const nums = new Array(n); // O(n)
    }
}
```

=== "Dart"

```dart title=""
void algorithm(int n) {
  int a = 0;                            // O(1)
  List<int> b = List.filled(10000, 0);  // O(1)
  if (n > 10) {
    List<int> nums = List.filled(n, 0); // O(n)
  }
}
```

=== "Rust"

```rust title=""
fn algorithm(n: i32) {
    let a = 0;                              // O(1)
    let b = [0; 10000];                     // O(1)
    if n > 10 {
        let nums = vec![0; n as usize];     // O(n)
    }
}
```

=== "C"

```c title=""
void algorithm(int n) {
    int a = 0;               // O(1)
    int b[10000];            // O(1)
    if (n > 10)
        int nums[n] = {0};   // O(n)
}
```

=== "Zig"

```zig title=""

```

In recursion functions, it is important to take into count the measurement of stack frame space. For example in the following code:

• The function loop() calls n times function() in a loop, and each round of function() returns and frees stack frame space, so the space complexity is still O(1).
• The recursion function recur() will have n unreturned recur() during runtime, thus occupying O(n) of stack frame space.

=== "Python"

```python title=""
def function() -> int:
    # Perform certain operations
    return 0

def loop(n: int):
    """Loop O(1)"""""
    for _ in range(n):
        function()

def recur(n: int) -> int:
    """Recursion O(n)"""""
    if n == 1: return
    return recur(n - 1)
```

=== "C++"

```cpp title=""
int func() {
    // Perform certain operations
    return 0;
}
/* Cycle O(1) */
void loop(int n) {
    for (int i = 0; i < n; i++) {
        func();
    }
}
/* Recursion O(n) */
void recur(int n) {
    if (n == 1) return;
    return recur(n - 1);
}
```

=== "Java"

```java title=""
int function() {
    // Perform certain operations
    return 0;
}
/* Cycle O(1) */
void loop(int n) {
    for (int i = 0; i < n; i++) {
        function();
    }
}
/* Recursion O(n) */
void recur(int n) {
    if (n == 1) return;
    return recur(n - 1);
}
```

=== "C#"

```csharp title=""
int Function() {
    // Perform certain operations
    return 0;
}
/* Cycle O(1) */
void Loop(int n) {
    for (int i = 0; i < n; i++) {
        Function();
    }
}
/* Recursion O(n) */
int Recur(int n) {
    if (n == 1) return 1;
    return Recur(n - 1);
}
```

=== "Go"

```go title=""
func function() int {
    // Perform certain operations
    return 0
}

/* Cycle O(1) */
func loop(n int) {
    for i := 0; i < n; i++ {
        function()
    }
}

/* Recursion O(n) */
func recur(n int) {
    if n == 1 {
        return
    }
    recur(n - 1)
}
```

=== "Swift"

```swift title=""
@discardableResult
func function() -> Int {
    // Perform certain operations
    return 0
}

/* Cycle O(1) */
func loop(n: Int) {
    for _ in 0 ..< n {
        function()
    }
}

/* Recursion O(n) */
func recur(n: Int) {
    if n == 1 {
        return
    }
    recur(n: n - 1)
}
```

=== "JS"

```javascript title=""
function constFunc() {
    // Perform certain operations
    return 0;
}
/* Cycle O(1) */
function loop(n) {
    for (let i = 0; i < n; i++) {
        constFunc();
    }
}
/* Recursion O(n) */
function recur(n) {
    if (n === 1) return;
    return recur(n - 1);
}
```

=== "TS"

```typescript title=""
function constFunc(): number {
    // Perform certain operations
    return 0;
}
/* Cycle O(1) */
function loop(n: number): void {
    for (let i = 0; i < n; i++) {
        constFunc();
    }
}
/* Recursion O(n) */
function recur(n: number): void {
    if (n === 1) return;
    return recur(n - 1);
}
```

=== "Dart"

```dart title=""
int function() {
  // Perform certain operations
  return 0;
}
/* Cycle O(1) */
void loop(int n) {
  for (int i = 0; i < n; i++) {
    function();
  }
}
/* Recursion O(n) */
void recur(int n) {
  if (n == 1) return;
  return recur(n - 1);
}
```

=== "Rust"

```rust title=""
fn function() -> i32 {
    // Perform certain operations
    return 0;
}
/* Cycle O(1) */
fn loop(n: i32) {
    for i in 0..n {
        function();
    }
}
/* Recursion O(n) */
void recur(n: i32) {
    if n == 1 {
        return;
    }
    recur(n - 1);
}
```

=== "C"

```c title=""
int func() {
    // Perform certain operations
    return 0;
}
/* Cycle O(1) */
void loop(int n) {
    for (int i = 0; i < n; i++) {
        func();
    }
}
/* Recursion O(n) */
void recur(int n) {
    if (n == 1) return;
    return recur(n - 1);
}
```

=== "Zig"

```zig title=""

```

Common Types

Assuming the input data size is n, the figure illustrates common types of space complexity (ordered from low to high).

$$ \begin{aligned} O(1) < O(\log n) < O(n) < O(n^2) < O(2^n) \newline \text{constant order} < \text{logarithmic order} < \text{linear order} < \text{square order} < \text{exponential order} \end{aligned}

Constant Order O(1)

Constant order is common for constants, variables, and objects whose quantity is unrelated to the size of the input data n.

It is important to note that memory occupied by initializing a variable or calling a function in a loop is released once the next iteration begins. Therefore, there is no accumulation of occupied space and the space complexity remains O(1) :

[file]{space_complexity}-[class]{}-[func]{constant}

Linear Order O(N)

Linear order is commonly found in arrays, linked lists, stacks, queues, and similar structures where the number of elements is proportional to n:

[file]{space_complexity}-[class]{}-[func]{linear}

As shown in the figure below, the depth of recursion for this function is n, which means that there are n unreturned linear_recur() functions at the same time, using O(n) size stack frame space:

[file]{space_complexity}-[class]{}-[func]{linear_recur}

Quadratic Order O(N^2)

Quadratic order is common in matrices and graphs, where the number of elements is in a square relationship with n:

[file]{space_complexity}-[class]{}-[func]{quadratic}

As shown in the figure below, the recursion depth of this function is n, and an array is initialized in each recursion function with lengths n, n-1, \dots, 2, 1, and an average length of n / 2, thus occupying O(n^2) space overall:

[file]{space_complexity}-[class]{}-[func]{quadratic_recur}

Exponential Order O(2^N)

Exponential order is common in binary trees. Looking at the figure below, a "full binary tree" of degree n has 2^n - 1 nodes, occupying O(2^n) space:

[file]{space_complexity}-[class]{}-[func]{build_tree}

Logarithmic Order O(\Log N)

Logarithmic order is commonly used in divide and conquer algorithms. For example, in a merge sort, given an array of length n as the input, each round of recursion divides the array in half from its midpoint to form a recursion tree of height \log n, using O(\log n) stack frame space.

Another example is to convert a number into a string. Given a positive integer n with a digit count of \log_{10} n + 1, the corresponding string length is \log_{10} n + 1. Therefore, the space complexity is O(\log_{10} n + 1) = O(\log n).

Weighing Time And Space

Ideally, we would like to optimize both the time complexity and the space complexity of an algorithm. However, in reality, simultaneously optimizing time and space complexity is often challenging.

Reducing time complexity usually comes at the expense of increasing space complexity, and vice versa. The approach of sacrificing memory space to improve algorithm speed is known as "trading space for time", while the opposite is called "trading time for space".

The choice between these approaches depends on which aspect we prioritize. In most cases, time is more valuable than space, so "trading space for time" is usually the more common strategy. Of course, in situations with large data volumes, controlling space complexity is also crucial.