Semifinal 2 was much tougher than semifinal 1, with only one contestant correctly finishing two problems. The first two problems each needed a key insight, after which very little coding is needed (assuming one has the appropriate library code available). The hard problem was more traditional, requiring a serious of smaller steps to work towards the solution.

### Easy

Consider the smallest special clique. What happens if we remove an element? Then we will still have a clique, but the AND of all the elements is non-zero. In particular, there is some bit position which is 1 in every element of the special clique except for 1.

We can turn that around: it suffices to find a bit position B, a number T which is zero on bit B, and a special clique containing T whose other elements are all 1 on bit B. We can easily make a list of candidates to put in this special clique: any element which is 1 on bit B and which is connected to T. What's more, these candidates are all connected to each other (through bit B), so taking T plus all candidates forms a clique. What's more, if we can make a special clique by using only a subset of the candidates, then the clique formed using all candidates will be special too, so the latter is the only one we need to test.

### Medium

The key insight is to treat this as a flow/matching problem. Rather than thinking of filling and emptying an unlabelled tree, we can just build a labelled tree with the properties that if A's parent is B, then B < A and A appears earlier in p than B does. A few minutes thinking should convince you that any labelled tree satisfying these constraints can be used for the sorting.

We thus need to match each number to a parent (either another number or the root). We can set this up with a bipartite graph in the usual way: each number appears on the left, connected to a source (for network flow) with an edge of capacity 1. Each number also appears on the right, along with the root, all connected to the sink with N edges of capacity 1 (we'll see later why I don't say one edge of capacity N). Between the two sides, we add an edge A → B if B is a viable parent for A, again with capacity 1. Each valid assignment can be represented by a maxflow on this network (with flow N), where the edges across the middle are parent-child relationships. Note that normally using this approach to building a tree risks creating cycles, but the heap requirement on the tree makes that impossible.

That tells us which trees are viable, but we still need to incorporate the cost function. That is where the edges from the right numbers to the sink come in. A (non-root) vertex starts with a cost of 1, then adding more children increases the cost by 3, 5, 7, ... Thus, we set the costs of the edges from each number to the sink to this sequence. Similarly, the costs of the edges from the root to the sink have costs 1, 3, 5, 7, ... The min-cost max-flow will automatically ensure that the cheaper edges get used first, so the cost of the flow will match the cost of the tree (after accounting for the initial cost of 1 for each number, which can be handled separately).

### Hard

This one requires a fairly solid grasp on linear algebra and vector spaces. The set with \(2^k\) elements is a vector subspace of \(\mathbb{Z}_2^N\) of dimension k, for some unknown N. It will have a basis of size k. To deal more easily with ordering, we will choose to consider a particular canonical basis. Given an arbitrary initial basis, one can use Gaussian reduction and back propagation to obtain a (unique) basis with the following property: the leading 1 bit in each basis element is a 0 bit in every other basis element. With this basis, it is not difficult to see that including any basis element in a sum will increase rather than decrease the sum. The combination of basis elements forming the ith sorted element is thus given exactly by the binary representation of i.

We can now take the information we're given an recast it is a set of linear equations. Furthermore, we can perform Gaussian elimination on these linear equations (which we actually do in code, unlike the thought experiment Gaussian elimination above). This leaves some basis elements fixed (uniquely determined by the smaller basis elements) and others free. Because of the constraints of the basis elements, we also get some more information. If basis i appears in an equation, its leading bit must correspond to a 1 bit in the value (and to the first 1 bit, if i is the leading basis in the equation). Similarly, if basis i does not appear, then it must correspond to a 0 bit in the value. This allows us to build a mask of where the leading bit for each basis element can be.

We now switch to dynamic programming to complete the count. We count the number of ways to assign the first B basis elements using the first C value bits. Clearly dp[0][C] = 1. To add new basis, we can consider the possible positions of its leading bit (within the low C bits), and then count the degrees of freedom for the remaining bits. If the basis is fixed then there is 1 degree of freedom; otherwise, there is a degree of freedom for each bit that isn't the leading bit of a previous basis, and it is easy to count these.

There are a few details that remain to do with checking whether there are an infinite number of solutions (which can only happen if the largest basis is unconstrained), and distinguishing between a true 0 and a 0 that is actually a multiple of 1000000007, but those are left as exercises for the reader.

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