Interval Scheduling/Partitioning

Scheduling the max number of intervals

Algorithm: sort by finish time and select the next compatible interval
Proof: Greedy stays ahead, by induction
Claim: Greedy algorithm is optimal
Lemma
- $P(r)$: For greedy choices $g_1, \ldots, g_n$ and optimal choices $k_1, \ldots, k_m$, $f(g_r) \le f(k_r)$
- $P(1)$: $g_1$ is chosen to have the minimum finish time, so $f(g_1) \le f(k_1)$
- Suppose $P(r)$. Since $f(g_r) \le f(k_r) \le s(k_{r + 1})$, $k_{r + 1}$ is among the candidates considered for $g_{r + 1}$. Of those candidates, it picks the minimum finish time, so $f(g_{r + 1}) \le f(k_{r + 1})$.
By this lemma, we must have $n \ge m$, since since otherwise $k_{n + 1}$ is in the set of candidates for $g_{n + 1}$.

Algorithm: sort intervals by start time, adding them to any compatible set. If no set is compatible, create a new one
Proof: exploit structural property
Claim: greedy algorithm is optimal
Let $d$ be the number of sets the greedy algorithm allocates. The $d$th set, $S_d$ is allocated because we had to assign some interval, $I_i$, that was not compatible with any of the $d - 1$ previous sets.
Since we sorted by start time, all intervals $I_j \in S_1 \cup \ldots \cup S_{d - 1}$ have $s(I_i) \ge s(I_j)$. Thus, we have at least depth $d$ intervals, and so all valid partitions must have $\ge d$ sets.