2769. Find the Maximum Achievable Number
Problem Description
In this problem, we are provided with two integers, num
and t
. We have to understand the concept of an "achievable" integer x
. An integer x
is considered achievable if we can make it equal to num
by using a certain operation at most t
times. This operation involves changing the value of x
by 1
(either increasing or decreasing it) and simultaneously changing num
by 1
in the opposite direction (if x
is decreased, num
is increased, and vice versa). Our task is to find the maximum possible achievable integer x
.
To solve this, we need to think about how the operation affects the relationship between x
and num
. Each operation changes the difference between x
and num
by 2 units. Because we have the freedom to do this t
times, we must evaluate how far we can push x
away from num
initially so that after at most t
operations they can still become equal.
Intuition
To derive the intuition behind the solution, it's crucial to look at the effect of the operation on the difference between x
and num
. Since each operation can be performed at most t
times, we want to maximize x
before we start performing operations.
If we increase x
by 1
and decrease num
by 1
, the difference between them increases by 2
. Conversely, if we decrease x
by 1
and increase num
by 1
, the difference decreases by 2
. Since we're looking for the maximum achievable x
, we'll want to increase x
as much as possible before hitting the limit of t
operations. Since t
is the maximum number of times we can perform this operation, the farthest we can stretch x
from num
initially is by t
operations, each of which will increase the difference by 2
. Therefore, the maximum achievable x
can simply be calculated by taking num
and adding t*2
to it.
Thus, even without running through the operations, we can directly find the maximum achievable x
by this straightforward mathematical relationship.
Learn more about Math patterns.
Solution Approach
The given problem is simple in nature and doesn't require complex algorithms or data structures. It's essentially a mathematical problem that we solve through an understanding of integers and their relationships after a series of operations.
Given that we can either increase or decrease x
by 1 and simultaneously do the opposite to num
in the same operation, we realize that each operation will either widen or narrow the gap between x
and num
by 2. To reach the highest achievable x
, we need to maximize x
from the onset.
As per the Reference Solution Approach, we see that this operation of increasing x
by 1 and decreasing num
by 1 (or vice versa) can be done up to t
times. Therefore, to maximize x
, we would theoretically perform all t
operations in the direction that increases x
(and decreases num
).
The solution code implements this straightforward insight:
class Solution:
def theMaximumAchievableX(self, num: int, t: int) -> int:
# By performing the operation t times, each time increasing x by 1,
# the maximum achievable x is calculated.
return num + t * 2
Here, we take the original num
and add t * 2
to account for t
operations, each increasing the initial value of x
by 2. There's no iteration, conditional logic, or use of additional space for data structures, making the entire solution a single mathematical expression.
In conclusion, the solution relies on the understanding that each operation can happen t
times, changing the value of x
by a total of t * 2
. We immediately return num + t * 2
as the result, representing the maximum achievable x
. This simplicity is what makes the problem solvable in constant time and space complexity, i.e., O(1).
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Start EvaluatorExample Walkthrough
Let's walk through a small example to illustrate the solution approach. Suppose we have num = 10
and t = 3
. Our goal is to find the maximum possible achievable integer x
.
Recall that the operation we can perform changes x
by 1
and simultaneously changes num
by 1
in the opposite direction. Let's see how we can use this operation to our advantage:
- We start with
num = 10
and we choose an initial value forx
. As we want to find the maximum achievablex
, it's intuitive to start by increasingx
. - For the first operation, if we increase
x
by1
(makingx
initially 11), we have to decreasenum
by1
(makingnum
now 9). - After the first operation, the difference between
x
andnum
is11 - 9 = 2
. We can perform the operation two more times sincet=3
. - For the second operation, we increase
x
by1
to 12 and decreasenum
by1
to 8. The difference is now12 - 8 = 4
. - Finally, for the third operation, we increase
x
by1
to 13 and decreasenum
by1
to 7. The difference is now13 - 7 = 6
.
Since we have now performed the operation t
times (which is 3 times in our case), we have reached our limit, and the resulting value of x
is the maximum achievable. Every time we performed an operation, we increased the difference by 2
, and since we've done that t
times, the maximum increase in x
compared to the original num
is t * 2
.
In our example, the maximum achievable x
is therefore 10 + (3 * 2) = 16
.
This simple example illustrates that we can calculate the maximum achievable x
, without having to actually increment and decrement x
and num
step by step, by simply taking the original num
and adding t * 2
to it, as shown in the solution code. Thus, our answer for this example is x = 16
.
Solution Implementation
1class Solution:
2 def theMaximumAchievableX(self, num: int, t: int) -> int:
3 # This method calculates the maximum value achievable for 'x'
4 # by adding the given number 'num' and twice the value of 't'.
5
6 # Calculate the result by adding 'num' with double the value of 't'
7 result = num + t * 2
8
9 # Return the computed result
10 return result
11
1class Solution {
2
3 // Method to calculate the maximum achievable value of X.
4 // Parameters:
5 // int num: The base number from which we start.
6 // int t: The number of times we perform the operation to increase 'num'.
7 // Returns:
8 // The maximum achievable value of X after performing the operation 't' times.
9 public int theMaximumAchievableX(int num, int t) {
10 // The operation consists of doubling the increment each time (hence, t * 2)
11 // Add the total increment to the original 'num' to get the maximum achievable X.
12 return num + t * 2;
13 }
14}
15
1class Solution {
2public:
3 // Function to calculate the maximum achievable value of x
4 int theMaximumAchievableX(int num, int t) {
5 // The formula to compute the maximum achievable value
6 int maximizedX = num + t * 2;
7
8 // Return the calculated value
9 return maximizedX;
10 }
11};
12
1// This function calculates the maximum achievable value of x given a number 'num' and a multiplier 't'.
2// The value of 'x' is found by adding 'num' to twice the value of 't'.
3// @param {number} num - The base number to which the result of 't * 2' will be added.
4// @param {number} t - The multiplier that is used to find the additional value to add to 'num'.
5// @returns {number} - The maximum achieved value of 'x'.
6
7function theMaximumAchievableX(num: number, t: number): number {
8 // Calculate the result by adding num to twice the value of t and return it.
9 return num + t * 2;
10}
11
Time and Space Complexity
The time complexity of the given code is O(1)
. This is because the operation num + t * 2
is performed in constant time, regardless of the size of the input values for num
and t
.
Similarly, the space complexity of the code is also O(1)
. The function does not allocate any additional space that grows with the input size. It only uses a fixed amount of space to store the input parameters and the result of the computation.
Learn more about how to find time and space complexity quickly using problem constraints.
What's the output of running the following function using input [30, 20, 10, 100, 33, 12]
?
1def fun(arr: List[int]) -> List[int]:
2 import heapq
3 heapq.heapify(arr)
4 res = []
5 for i in range(3):
6 res.append(heapq.heappop(arr))
7 return res
8
1public static int[] fun(int[] arr) {
2 int[] res = new int[3];
3 PriorityQueue<Integer> heap = new PriorityQueue<>();
4 for (int i = 0; i < arr.length; i++) {
5 heap.add(arr[i]);
6 }
7 for (int i = 0; i < 3; i++) {
8 res[i] = heap.poll();
9 }
10 return res;
11}
12
1class HeapItem {
2 constructor(item, priority = item) {
3 this.item = item;
4 this.priority = priority;
5 }
6}
7
8class MinHeap {
9 constructor() {
10 this.heap = [];
11 }
12
13 push(node) {
14 // insert the new node at the end of the heap array
15 this.heap.push(node);
16 // find the correct position for the new node
17 this.bubble_up();
18 }
19
20 bubble_up() {
21 let index = this.heap.length - 1;
22
23 while (index > 0) {
24 const element = this.heap[index];
25 const parentIndex = Math.floor((index - 1) / 2);
26 const parent = this.heap[parentIndex];
27
28 if (parent.priority <= element.priority) break;
29 // if the parent is bigger than the child then swap the parent and child
30 this.heap[index] = parent;
31 this.heap[parentIndex] = element;
32 index = parentIndex;
33 }
34 }
35
36 pop() {
37 const min = this.heap[0];
38 this.heap[0] = this.heap[this.size() - 1];
39 this.heap.pop();
40 this.bubble_down();
41 return min;
42 }
43
44 bubble_down() {
45 let index = 0;
46 let min = index;
47 const n = this.heap.length;
48
49 while (index < n) {
50 const left = 2 * index + 1;
51 const right = left + 1;
52
53 if (left < n && this.heap[left].priority < this.heap[min].priority) {
54 min = left;
55 }
56 if (right < n && this.heap[right].priority < this.heap[min].priority) {
57 min = right;
58 }
59 if (min === index) break;
60 [this.heap[min], this.heap[index]] = [this.heap[index], this.heap[min]];
61 index = min;
62 }
63 }
64
65 peek() {
66 return this.heap[0];
67 }
68
69 size() {
70 return this.heap.length;
71 }
72}
73
74function fun(arr) {
75 const heap = new MinHeap();
76 for (const x of arr) {
77 heap.push(new HeapItem(x));
78 }
79 const res = [];
80 for (let i = 0; i < 3; i++) {
81 res.push(heap.pop().item);
82 }
83 return res;
84}
85
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