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Minimalus ištrynimų skaičius, kad būtų galima surūšiuoti seką

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Duotas n sveikųjų skaičių masyvas. Užduotis yra pašalinti arba ištrinti minimalų skaičių elementų iš masyvo, kad likusieji elementai būtų išdėstyti ta pačia seka, kad susidarytų didėjanti rūšiuojama seka .

Pavyzdžiai:  

Input : {5 6 1 7 4}  
Output : 2
Removing 1 and 4
leaves the remaining sequence order as
5 6 7 which is a sorted sequence.
Input : {30 40 2 5 1 7 45 50 8}
Output : 4
Recommended Practice Minimalus ištrynimų skaičius, kad būtų galima surūšiuoti seką Išbandykite!


A paprastas sprendimas yra pašalinti visas pasekmes po vieną ir patikrinkite, ar likęs elementų rinkinys yra surūšiuotas, ar ne. Šio sprendimo sudėtingumas laike yra eksponentinis.



An efektyvus požiūris vartoja sąvoką ilgiausios didėjančios posekos ilgio radimas tam tikros sekos.

Algoritmas:  

-->    arr    be the given array.  
--> n number of elements in arr .
--> len be the length of longest
increasing subsequence in arr .
-->// minimum number of deletions
min = n - len
C++
// C++ implementation to find  // minimum number of deletions  // to make a sorted sequence #include    using namespace std; /* lis() returns the length  of the longest increasing   subsequence in arr[] of size n */ int lis( int arr[] int n ) {  int result = 0;  int lis[n];  /* Initialize LIS values  for all indexes */  for (int i = 0; i < n; i++ )  lis[i] = 1;  /* Compute optimized LIS   values in bottom up manner */  for (int i = 1; i < n; i++ )  for (int j = 0; j < i; j++ )  if ( arr[i] > arr[j] &&  lis[i] < lis[j] + 1)  lis[i] = lis[j] + 1;  /* Pick resultimum   of all LIS values */  for (int i = 0; i < n; i++ )  if (result < lis[i])  result = lis[i];  return result; } // function to calculate minimum // number of deletions int minimumNumberOfDeletions(int arr[]   int n) {  // Find longest increasing   // subsequence  int len = lis(arr n);  // After removing elements   // other than the lis we   // get sorted sequence.  return (n - len); } // Driver Code int main() {  int arr[] = {30 40 2 5 1  7 45 50 8};  int n = sizeof(arr) / sizeof(arr[0]);  cout << 'Minimum number of deletions = '  << minimumNumberOfDeletions(arr n);  return 0; } 
Java
// Java implementation to find // minimum number of deletions  // to make a sorted sequence class GFG {  /* lis() returns the length   of the longest increasing   subsequence in arr[] of size n */  static int lis( int arr[] int n )  {  int result = 0;  int[] lis = new int[n];    /* Initialize LIS values   for all indexes */  for (int i = 0; i < n; i++ )  lis[i] = 1;    /* Compute optimized LIS   values in bottom up manner */  for (int i = 1; i < n; i++ )  for (int j = 0; j < i; j++ )  if ( arr[i] > arr[j] &&  lis[i] < lis[j] + 1)  lis[i] = lis[j] + 1;    /* Pick resultimum of  all LIS values */  for (int i = 0; i < n; i++ )  if (result < lis[i])  result = lis[i];    return result;  }    // function to calculate minimum  // number of deletions  static int minimumNumberOfDeletions(int arr[]   int n)  {  // Find longest   // increasing subsequence  int len = lis(arr n);    // After removing elements   // other than the lis we get  // sorted sequence.  return (n - len);  }  // Driver Code  public static void main (String[] args)   {  int arr[] = {30 40 2 5 1  7 45 50 8};  int n = arr.length;  System.out.println('Minimum number of' +  ' deletions = ' +  minimumNumberOfDeletions(arr n));  } } /* This code is contributed by Harsh Agarwal */ 
Python3
# Python3 implementation to find  # minimum number of deletions to # make a sorted sequence # lis() returns the length  # of the longest increasing # subsequence in arr[] of size n def lis(arr n): result = 0 lis = [0 for i in range(n)] # Initialize LIS values # for all indexes  for i in range(n): lis[i] = 1 # Compute optimized LIS values  # in bottom up manner  for i in range(1 n): for j in range(i): if ( arr[i] > arr[j] and lis[i] < lis[j] + 1): lis[i] = lis[j] + 1 # Pick resultimum  # of all LIS values  for i in range(n): if (result < lis[i]): result = lis[i] return result # Function to calculate minimum # number of deletions def minimumNumberOfDeletions(arr n): # Find longest increasing  # subsequence len = lis(arr n) # After removing elements  # other than the lis we  # get sorted sequence. return (n - len) # Driver Code arr = [30 40 2 5 1 7 45 50 8] n = len(arr) print('Minimum number of deletions = ' minimumNumberOfDeletions(arr n)) # This code is contributed by Anant Agarwal. 
C#
// C# implementation to find // minimum number of deletions  // to make a sorted sequence using System; class GfG  {    /* lis() returns the length of  the longest increasing subsequence  in arr[] of size n */  static int lis( int []arr int n )  {  int result = 0;  int[] lis = new int[n];    /* Initialize LIS values for  all indexes */  for (int i = 0; i < n; i++ )  lis[i] = 1;    /* Compute optimized LIS values  in bottom up manner */  for (int i = 1; i < n; i++ )  for (int j = 0; j < i; j++ )  if ( arr[i] > arr[j] &&  lis[i] < lis[j] + 1)  lis[i] = lis[j] + 1;    /* Pick resultimum of all LIS  values */  for (int i = 0; i < n; i++ )  if (result < lis[i])  result = lis[i];    return result;  }    // function to calculate minimum  // number of deletions  static int minimumNumberOfDeletions(  int []arr int n)  {    // Find longest increasing  // subsequence  int len = lis(arr n);    // After removing elements other  // than the lis we get sorted  // sequence.  return (n - len);  }  // Driver Code  public static void Main (String[] args)   {  int []arr = {30 40 2 5 1   7 45 50 8};  int n = arr.Length;  Console.Write('Minimum number of' +   ' deletions = ' +   minimumNumberOfDeletions(arr n));  } } // This code is contributed by parashar. 
JavaScript
<script> // javascript implementation to find // minimum number of deletions // to make a sorted sequence /* lis() returns the length of the longest increasing subsequence in arr[] of size n */ function lis(arrn) {  let result = 0;  let lis= new Array(n);  /* Initialize LIS values  for all indexes */  for (let i = 0; i < n; i++ )  lis[i] = 1;  /* Compute optimized LIS  values in bottom up manner */  for (let i = 1; i < n; i++ )  for (let j = 0; j < i; j++ )  if ( arr[i] > arr[j] &&  lis[i] < lis[j] + 1)  lis[i] = lis[j] + 1;  /* Pick resultimum  of all LIS values */  for (let i = 0; i < n; i++ )  if (result < lis[i])  result = lis[i];  return result; } // function to calculate minimum // number of deletions function minimumNumberOfDeletions(arrn) {  // Find longest increasing  // subsequence  let len = lis(arrn);  // After removing elements  // other than the lis we  // get sorted sequence.  return (n - len); }  let arr = [30 40 2 5 17 45 50 8];  let n = arr.length;  document.write('Minimum number of deletions = '  + minimumNumberOfDeletions(arrn)); // This code is contributed by vaibhavrabadiya117. </script> 
PHP
 // PHP implementation to find  // minimum number of deletions  // to make a sorted sequence /* lis() returns the length of   the longest increasing subsequence  in arr[] of size n */ function lis( $arr $n ) { $result = 0; $lis[$n] = 0; /* Initialize LIS values  for all indexes */ for ($i = 0; $i < $n; $i++ ) $lis[$i] = 1; /* Compute optimized LIS   values in bottom up manner */ for ($i = 1; $i < $n; $i++ ) for ($j = 0; $j < $i; $j++ ) if ( $arr[$i] > $arr[$j] && $lis[$i] < $lis[$j] + 1) $lis[$i] = $lis[$j] + 1; /* Pick resultimum of   all LIS values */ for ($i = 0; $i < $n; $i++ ) if ($result < $lis[$i]) $result = $lis[$i]; return $result; } // function to calculate minimum // number of deletions function minimumNumberOfDeletions($arr $n) { // Find longest increasing // subsequence $len = lis($arr $n); // After removing elements  // other than the lis we // get sorted sequence. return ($n - $len); } // Driver Code $arr = array(30 40 2 5 1 7 45 50 8); $n = sizeof($arr) / sizeof($arr[0]); echo 'Minimum number of deletions = '  minimumNumberOfDeletions($arr $n); // This code is contributed by nitin mittal. ?> 

Išvestis
Minimum number of deletions = 4

Laiko sudėtingumas: O (n2)
Pagalbinė erdvė: O(n)

stygų metodai java

Laiko sudėtingumą galima sumažinti iki O (nlogn) suradus Ilgiausiai didėjančios sekos dydis (N Log N)
Prie šio straipsnio prisidėjo Ayush Jauhari .

2 metodas: naudojant ilgiausią didėjančią seką

Vienas iš būdų išspręsti šią problemą yra rasti nurodyto masyvo ilgiausios didėjančios posekos (LIS) ilgį ir atimti jį iš masyvo ilgio. Skirtumas suteikia mums mažiausią ištrynimų skaičių, reikalingą, kad masyvas būtų surūšiuotas.

Algoritmas

1. Apskaičiuokite ilgiausios masyvo didėjančios posekos (LIS) ilgį.
2. Iš masyvo ilgio atimkite LIS ilgį.
3. 2 veiksme gautą skirtumą grąžinkite kaip išvestį.

C++
#include    #include  #include    // Required for max_element using namespace std; // Function to find the minimum number of deletions int minDeletions(vector<int> arr) {  int n = arr.size();  vector<int> lis(n 1); // Initialize LIS array with 1    // Calculate LIS values  for (int i = 1; i < n; ++i) {  for (int j = 0; j < i; ++j) {  if (arr[i] > arr[j]) {  lis[i] = max(lis[i] lis[j] + 1); // Update LIS value  }  }  }    // Find the maximum length of LIS  int maxLength = *max_element(lis.begin() lis.end());    // Return the minimum number of deletions  return n - maxLength; } //Driver code int main() {  vector<int> arr = {5 6 1 7 4};    // Call the minDeletions function and print the result  cout << minDeletions(arr) << endl;    return 0; } 
Java
import java.util.Arrays; public class Main {  public static int minDeletions(int[] arr) {  int n = arr.length;  int[] lis = new int[n];  Arrays.fill(lis 1); // Initialize the LIS array with all 1's    for (int i = 1; i < n; i++) {  for (int j = 0; j < i; j++) {  if (arr[i] > arr[j]) {  lis[i] = Math.max(lis[i] lis[j] + 1);  }  }  }    return n - Arrays.stream(lis).max().getAsInt(); // Return the number of elements to delete  }  public static void main(String[] args) {  int[] arr = {5 6 1 7 4};  System.out.println(minDeletions(arr)); // Output: 2  } } 
Python3
def min_deletions(arr): n = len(arr) lis = [1] * n for i in range(1 n): for j in range(i): if arr[i] > arr[j]: lis[i] = max(lis[i] lis[j] + 1) return n - max(lis) arr = [5 6 1 7 4] print(min_deletions(arr)) 
C#
using System; using System.Collections.Generic; using System.Linq; namespace MinDeletionsExample {  class Program  {  static int MinDeletions(List<int> arr)  {  int n = arr.Count;  List<int> lis = Enumerable.Repeat(1 n).ToList(); // Initialize LIS array with 1  // Calculate LIS values  for (int i = 1; i < n; ++i)  {  for (int j = 0; j < i; ++j)  {  if (arr[i] > arr[j])  {  lis[i] = Math.Max(lis[i] lis[j] + 1); // Update LIS value  }  }  }  // Find the maximum length of LIS  int maxLength = lis.Max();  // Return the minimum number of deletions  return n - maxLength;  }  // Driver Code  static void Main(string[] args)  {  List<int> arr = new List<int> { 5 6 1 7 4 };  // Call the MinDeletions function and print the result  Console.WriteLine(MinDeletions(arr));  // Keep console window open until a key is pressed  Console.ReadKey();  }  } } 
JavaScript
function minDeletions(arr) {  let n = arr.length;  let lis = new Array(n).fill(1);  for (let i = 1; i < n; i++) {  for (let j = 0; j < i; j++) {  if (arr[i] > arr[j]) {  lis[i] = Math.max(lis[i] lis[j] + 1);  }  }  }  return n - Math.max(...lis); } let arr = [5 6 1 7 4]; console.log(minDeletions(arr));  

Išvestis
2

Laiko sudėtingumas: O(n^2), kur n yra masyvo ilgis
Erdvės sudėtingumas: O(n), kur n yra masyvo ilgis

10 iš 40

3 metodas: dvejetainės paieškos naudojimas

Šis metodas naudoja dvejetainę paiešką, kad surastų tinkamą vietą tam tikram elementui įterpti į poseką.

Algoritmas

1. Inicijuokite sąrašo „antrinį“ su pirmuoju įvesties sąrašo elementu.
2. Kiekvienam paskesniam įvesties sąrašo elementui, jei jis didesnis nei paskutinis elementas skiltyje „sub“, pridėkite jį prie „sub“.
3. Kitu atveju naudokite dvejetainę paiešką, kad rastumėte tinkamą vietą elementui įterpti į „sub“.
4. Minimalus reikalaujamas ištrynimų skaičius yra lygus įvesties sąrašo ilgiui, atėmus „sub“ ilgį.

C++
#include    #include  using namespace std; // Function to find the minimum number of deletions to make a strictly increasing subsequence int minDeletions(vector<int>& arr) {  int n = arr.size();  vector<int> sub; // Stores the longest increasing subsequence    sub.push_back(arr[0]); // Initialize the subsequence with the first element of the array    for (int i = 1; i < n; i++) {  if (arr[i] > sub.back()) {  // If the current element is greater than the last element of the subsequence  // it can be added to the subsequence to make it longer.  sub.push_back(arr[i]);  } else {  int index = -1; // Initialize index to -1  int val = arr[i]; // Current element value  int l = 0 r = sub.size() - 1; // Initialize left and right pointers for binary search    // Binary search to find the index where the current element can be placed in the subsequence  while (l <= r) {  int mid = (l + r) / 2; // Calculate the middle index    if (sub[mid] >= val) {  index = mid; // Update the index if the middle element is greater or equal to the current element  r = mid - 1; // Move the right pointer to mid - 1  } else {  l = mid + 1; // Move the left pointer to mid + 1  }  }    if (index != -1) {  sub[index] = val; // Replace the element at the found index with the current element  }  }  }  // The minimum number of deletions is equal to the difference between the input array size and the size of the longest increasing subsequence  return n - sub.size(); } int main() {  vector<int> arr = {30 40 2 5 1 7 45 50 8};  int output = minDeletions(arr);  cout << output << endl;    return 0; } 
Java
import java.util.ArrayList; public class Main {  // Function to find the minimum number of deletions to make a strictly increasing subsequence  static int minDeletions(ArrayList<Integer> arr) {  int n = arr.size();  ArrayList<Integer> sub = new ArrayList<>(); // Stores the longest increasing subsequence  sub.add(arr.get(0)); // Initialize the subsequence with the first element of the array  for (int i = 1; i < n; i++) {  if (arr.get(i) > sub.get(sub.size() - 1)) {  // If the current element is greater than the last element of the subsequence  // it can be added to the subsequence to make it longer.  sub.add(arr.get(i));  } else {  int index = -1; // Initialize index to -1  int val = arr.get(i); // Current element value  int l = 0 r = sub.size() - 1; // Initialize left and right pointers for binary search  // Binary search to find the index where the current element can be placed in the subsequence  while (l <= r) {  int mid = (l + r) / 2; // Calculate the middle index  if (sub.get(mid) >= val) {  index = mid; // Update the index if the middle element is greater or equal to the current element  r = mid - 1; // Move the right pointer to mid - 1  } else {  l = mid + 1; // Move the left pointer to mid + 1  }  }  if (index != -1) {  sub.set(index val); // Replace the element at the found index with the current element  }  }  }  // The minimum number of deletions is equal to the difference between the input array size and the size of the longest increasing subsequence  return n - sub.size();  }  public static void main(String[] args) {  ArrayList<Integer> arr = new ArrayList<>();  arr.add(30);  arr.add(40);  arr.add(2);  arr.add(5);  arr.add(1);  arr.add(7);  arr.add(45);  arr.add(50);  arr.add(8);  int output = minDeletions(arr);  System.out.println(output);  } } 
Python3
def min_deletions(arr): def ceil_index(sub val): l r = 0 len(sub)-1 while l <= r: mid = (l + r) // 2 if sub[mid] >= val: r = mid - 1 else: l = mid + 1 return l sub = [arr[0]] for i in range(1 len(arr)): if arr[i] > sub[-1]: sub.append(arr[i]) else: sub[ceil_index(sub arr[i])] = arr[i] return len(arr) - len(sub) arr = [30 40 2 5 1 7 45 50 8] output = min_deletions(arr) print(output) 
C#
using System; using System.Collections.Generic; class Program {  // Function to find the minimum number of deletions to make a strictly increasing subsequence  static int MinDeletions(List<int> arr)  {  int n = arr.Count;  List<int> sub = new List<int>(); // Stores the longest increasing subsequence  sub.Add(arr[0]); // Initialize the subsequence with the first element of the array  for (int i = 1; i < n; i++)  {  if (arr[i] > sub[sub.Count - 1])  {  // If the current element is greater than the last element of the subsequence  // it can be added to the subsequence to make it longer.  sub.Add(arr[i]);  }  else  {  int index = -1; // Initialize index to -1  int val = arr[i]; // Current element value  int l = 0 r = sub.Count - 1; // Initialize left and right   // pointers for binary search  // Binary search to find the index where the current element   // can be placed in the subsequence  while (l <= r)  {  int mid = (l + r) / 2; // Calculate the middle index  if (sub[mid] >= val)  {  index = mid; // Update the index if the middle element is   // greater or equal to the current element  r = mid - 1; // Move the right pointer to mid - 1  }  else  {  l = mid + 1; // Move the left pointer to mid + 1  }  }  if (index != -1)  {  sub[index] = val; // Replace the element at the found index   // with the current element  }  }  }  // The minimum number of deletions is equal to the difference   // between the input list size and the size of the   // longest increasing subsequence  return n - sub.Count;  } // Driver code  static void Main()  {  List<int> arr = new List<int> { 30 40 2 5 1 7 45 50 8 };  int output = MinDeletions(arr);  Console.WriteLine(output);  Console.ReadLine();  } } 
JavaScript
// Function to find the minimum number of deletions to make a strictly increasing subsequence function minDeletions(arr) {  let n = arr.length;  let sub = []; // Stores the longest increasing subsequence  sub.push(arr[0]); // Initialize the subsequence with the first element of the array  for (let i = 1; i < n; i++) {  if (arr[i] > sub[sub.length - 1]) {  // If the current element is greater than the last element of the subsequence  // it can be added to the subsequence to make it longer.  sub.push(arr[i]);  } else {  let index = -1; // Initialize index to -1  let val = arr[i]; // Current element value  let l = 0 r = sub.length - 1; // Initialize left and right pointers for binary search  // Binary search to find the index where the current element can be placed   // in the subsequence  while (l <= r) {  let mid = Math.floor((l + r) / 2); // Calculate the middle index  if (sub[mid] >= val) {  index = mid; // Update the index if the middle element is greater   //or equal to the current element  r = mid - 1; // Move the right pointer to mid - 1  } else {  l = mid + 1; // Move the left pointer to mid + 1  }  }  if (index !== -1) {  sub[index] = val; // Replace the element at the found index with the current element  }  }  }  // The minimum number of deletions is equal to the difference  //between the input array size and the size of the longest increasing subsequence  return n - sub.length; } let arr = [30 40 2 5 1 7 45 50 8]; let output = minDeletions(arr); console.log(output); 

Išvestis
4

Laiko sudėtingumas: O(n log n)

Pagalbinė erdvė: O(n)

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