You have a pointer at index 0
in an array of size arrLen
. At each step, you can move 1 position to the left, 1 position to the right, or stay in the same place (The pointer should not be placed outside the array at any time).
Given two integers steps
and arrLen
, return the number of ways such that your pointer is at index 0
after exactly steps
steps. Since the answer may be too large, return it modulo 10^9 + 7
.
Input: steps = 3, arrLen = 2
Output: 4
Explanation: There are 4 different ways to stay at index 0 after 3 steps:
Right, Left, Stay
Stay, Right, Left
Right, Stay, Left
Stay, Stay, Stay
Input: steps = 2, arrLen = 4
Output: 2
Explanation: There are 2 different ways to stay at index 0 after 2 steps:
Right, Left
Stay, Stay
Input: steps = 4, arrLen = 2
Output: 8
Explanation: There are 8 different ways to stay at index 0 after 4 steps.
To solve this problem, we need to:
Apply string manipulation concepts to solve a real-world problem.
You have a pointer at index 0
in an array of size arrLen
. At each step, you can move 1 position to the left, 1 position to the right, or stay in the same place (The pointer should not be placed outside the array at any time).
Given two integers steps
and arrLen
, return the number of ways such that your pointer is at index 0
after exactly steps
steps. Since the answer may be too large, return it modulo 10^9 + 7
.
There are 4 different ways to stay at index 0 after 3 steps: Right, Left, Stay Stay, Right, Left Right, Stay, Left Stay, Stay, Stay
There are 2 different ways to stay at index 0 after 2 steps: Right, Left Stay, Stay
There are 8 different ways to stay at index 0 after 4 steps.
This problem can be solved using dynamic programming.
The key insight is to define dp[i][j] as the number of ways to be at position j after i steps.
For each step i and position j, we can consider three possible moves: stay, move left, or move right.
The recurrence relation is: dp[i][j] = dp[i-1][j] + dp[i-1][j-1] + dp[i-1][j+1].
We can optimize the space complexity by observing that we only need to consider positions up to min(arrLen-1, steps), as we can't go further than steps positions away from the starting point.
This problem has several practical applications:
Modeling random walks in one dimension, which are used in physics, economics, and other fields to describe paths that consist of a succession of random steps.
Calculating the probability of returning to a starting point after a certain number of steps in a stochastic process.
Planning movement patterns in constrained environments, such as robots navigating in a linear space with boundaries.
You have a pointer at index 0
in an array of size arrLen
. At each step, you can move 1 position to the left, 1 position to the right, or stay in the same place (The pointer should not be placed outside the array at any time).
Given two integers steps
and arrLen
, return the number of ways such that your pointer is at index 0
after exactly steps
steps. Since the answer may be too large, return it modulo 10^9 + 7
.
Input: steps = 3, arrLen = 2
Output: 4
Explanation: There are 4 different ways to stay at index 0 after 3 steps:
Right, Left, Stay
Stay, Right, Left
Right, Stay, Left
Stay, Stay, Stay
Input: steps = 2, arrLen = 4
Output: 2
Explanation: There are 2 different ways to stay at index 0 after 2 steps:
Right, Left
Stay, Stay
Input: steps = 4, arrLen = 2
Output: 8
Explanation: There are 8 different ways to stay at index 0 after 4 steps.
To solve this problem, we need to:
Apply string manipulation concepts to solve a real-world problem.
You have a pointer at index 0
in an array of size arrLen
. At each step, you can move 1 position to the left, 1 position to the right, or stay in the same place (The pointer should not be placed outside the array at any time).
Given two integers steps
and arrLen
, return the number of ways such that your pointer is at index 0
after exactly steps
steps. Since the answer may be too large, return it modulo 10^9 + 7
.
There are 4 different ways to stay at index 0 after 3 steps: Right, Left, Stay Stay, Right, Left Right, Stay, Left Stay, Stay, Stay
There are 2 different ways to stay at index 0 after 2 steps: Right, Left Stay, Stay
There are 8 different ways to stay at index 0 after 4 steps.
This problem can be solved using dynamic programming.
The key insight is to define dp[i][j] as the number of ways to be at position j after i steps.
For each step i and position j, we can consider three possible moves: stay, move left, or move right.
The recurrence relation is: dp[i][j] = dp[i-1][j] + dp[i-1][j-1] + dp[i-1][j+1].
We can optimize the space complexity by observing that we only need to consider positions up to min(arrLen-1, steps), as we can't go further than steps positions away from the starting point.
This problem has several practical applications:
Modeling random walks in one dimension, which are used in physics, economics, and other fields to describe paths that consist of a succession of random steps.
Calculating the probability of returning to a starting point after a certain number of steps in a stochastic process.
Planning movement patterns in constrained environments, such as robots navigating in a linear space with boundaries.