Took me a minute to realize the greedy approach was fine here.
Clarify the problem constraints (minimum count, whether events have weights) and then use dynamic programming to determine the maximum number of non-adjacent events selectable. If the maximum is at least the minimum count, reconstruct one valid schedule by backtracking through the DP table.
Pro tip: Always discuss edge cases like empty input, minimum count of 0, or minimum count exceeding the maximum possible; these show thoroughness and often reveal hidden assumptions.
Ask about the minimum count, whether events have weights or values, and if the input list can be empty. Confirm that 'adjacent' means indices differ by exactly 1.
Let dp[i] be the maximum number of non-adjacent events selectable from the first i events. Recurrence: dp[i] = max(dp[i-1], 1 + dp[i-2]) with base cases dp[0]=0, dp[1]=1.
Iterate through the list to fill the DP table. After processing all events, check if dp[n] >= minimum count. If not, return that no valid selection exists.
Backtrack from i=n: if dp[i] == dp[i-1], skip event i; else include event i and move to i-2. Continue until i<=0. This yields a valid subset.
Return true and the reconstructed schedule, or false if infeasible. Mention time O(n) and space O(n), with possible O(1) space optimization if only count is needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.