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LIVE SESSION

AI INTERVIEWER

Can you explain the time complexity difference between your recursive and iterative solutions?

YOU

Analyzing...
Ends Session
solution.tstest.tsconsole

function fibonacci(n: number): number {

// Base cases

if (n <= 1) return n;

 

// Optimize with memoization

const dp = new Array(n + 1);

dp[0] = 0; dp[1] = 1;

 

for (let i = 2; i <= n; i++) {

dp[i] = dp[i-1] + dp[i-2];

}

 

return dp[n];

}

Tests Passed (3/3)
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LIVE SESSION 00:14:23
AI
That solution works, but it's O(n²). Can we optimize this using a hash map to get it down to linear time?
function twoSum(nums, target) {
  const map = new Map();
  // ... implementation ...
}
YOU
Yes, absolutely. By using a hash map, we can check for the complement in O(1) time as we iterate through the array.
Communication: ExcellentOptimization: Good
YOU
I'm stuck on the Dynamic Programming approach. I know I need a DP table, but what should the state be?
Let's break it down. Since we're trying to find the longest subsequence, what does our decision depend on at each index?

Hint: Think about what information you need from previous elements to extend the current subsequence.
YOU
Oh! I need to know the length of the LIS ending at the previous indices, and compare their values to the current one.
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