15-451 Algorithms 11/15/11 * Approximation Algorithms Reminder: quiz thurs - vertex cover Up to and including today - set cover 1 sheet of notes. - MAX-3-SAT handouts: practice quiz ====================================================================== APPROXIMATION ALGORITHMS ======================== General Question: Maybe we can't hope for a fast algorithm that always gets the best solution, but can we at least guarantee to get a "pretty good" solution? E.g., can we guarantee to find a solution that's within 10% of optimal? Or how about within a factor of 2 of optimal? Or, anything non-trivial? Interesting thing: even though all NP-complete problems are equivalent in terms of difficulty of finding optimal solution, the difficulty of getting a good approximation varies all over the map. VERTEX COVER ============ - GIVEN: a graph G. GOAL: find the smallest set of vertices such that every edge is incident to (touches) at least one vertex in the set. - Example: +----+----+ | | | +----+----+ - Can think of as: what is the fewest # of guards we need to place in museum to cover all the corridors. - This problem is NP-hard. [Can anyone see a reduction from CLIQUE?] - But it turns out that for any graph G, we can get within a factor of 2. Let's start with some strategies that *don't* work. Strawman #1: Pick arbitrary vertex with at least one uncovered edge incident, put into cover, and repeat. What would be a bad example? Strawman #2: how about picking the vertex that covers the *most* uncovered edges. Turns out this doesn't work either. [make bipartite graph where opt is size t, but this alg finds one of size t + floor(t/2) + floor(t/3) + floor(t/4) + ... + 1. This is Theta(t log t). Best examples are with t=6 or t=12.] How to get factor of 2. Two algorithms: Alg1: Pick arbitrary edge. We know any VC must have at least 1 endpt, so lets take both. Then throw out all edges covered and repeat. Keep going until no uncovered edges left. What we've found in the end is a matching (a set of edges no two of which share an endpoint) that's "maximal" (meaning that you can't add any more edges to it and keep it a matching). This means if we take all endpoints, we have a VC. So, if we picked k edges, our VC has size 2k. But, any VC must have size at least k since you need to have at least one endpoint of each edge (and, these edges don't touch, so these are k *different* vertices). Here's another 2-approximation algorithm for Vertex Cover: Alg2: Step1: Solve a *fractional* version of the problem. Have a variable x_i for each vertex. Constraint 0<= x_i <= 1. Think of x_i = 1 as picking the vertex, x_i = 0 as not picking it, and in-between as "partially picking it". Each edge should be covered in that for each edge (i,j) we want x_i+x_j >= 1. Then our goal is to minimize sum_i x_i. We can solve this using linear programming. This is called an "LP relaxation" because any true vertex cover is a feasible solution, but we've made the problem easier by allowing fractional solutions too. So, the value of the optimal solution now will be at least as good as the smallest vertex cover, maybe even better, but it just might not be legal any more. E.g., triangle-graph. E.g., star-graph. Step2: now pick each vertex i such that x_i >= 1/2. Claim 1: this is a VC. Why? Claim 2: The size of this VC is at most twice the size of the optimal VC. Why? Let OPT_frac be the value of the optimal fractional solution, and OPT_VC be the size of the smallest vertex cover. First, as we noted above, OPT_frac <= OPT_VC. Second, our solution is at most 2*OPT_frac since it's no worse than doubling and rounding down. So, put together, our solution <= 2*OPT_VC. Interesting fact: nobody knows any algorithm with approximation ratio 1.9. Best known is 2 - O(1/sqrt(log n)), which is 2 - o(1). Current best hardness result: Hastad shows 7/6 is NP-hard. Improved to 1.361 by Dinur and Safra. Beating 2-epsilon has been related to some other open problems, but not known to be NP-hard. SET-COVER --------- Set-cover: Given a domain X of n points, and m subsets S_1, S_2, ..., S_m of these points. Goal: find the fewest number of these subsets needed to cover all the points. Set-cover is NP-hard. However, there's a simple algorithm that gets a ratio of ln(n): Greedy Algorithm: Pick the set that covers the most points. Throw out all the points covered. Repeat. What's an example where this *doesn't* find the best solution? Theorem: If the optimal solution uses k sets, the greedy algorithm finds a solution with at most k*ln(n) sets. Proof: Since the optimal solution uses k sets, there must some set that covers at least a 1/k fraction of the points. Therefore, after the first iteration of the algorithm, there are at most n(1 - 1/k) points left. After the second, there are at most n(1 - 1/k)^2 points left, etc. After k rounds, there are at most n(1 - 1/k)^k < n*(1/e) points left. After k*ln(n) rounds, there are < n*(1/e)^{ln n} = 1 points left, which means we must be done. In fact, it's been proven that unless everything in NP can be solved in time n^{O(loglog n)}, then you can't get better than (1-epsilon)*ln(n) [Feige]. MAX-SAT: Given a CNF formula (AND of ORs, like in SAT), try to maximize the number of clauses satisfied. To make things cleaner, let's assume we have reduced each clause [so, (x or x or y) would become just (x or y), and (x or not(x)) would be removed] Claim: if every clause has size exactly 3 (this is sometimes called the MAX-exactly-3-SAT problem), then there is a simple randomized algorithm can satisfy at least a 7/8 fraction of clauses. (So, this for sure would be at least a 7/8-approximation to the optimum.) Proof: Just try a random assignment to the variables. Each clause has a 7/8 chance of being satisfied. So if there are m clauses total, the expected number satisfied is (7/8)m. If the assignment satisfies less, just repeat. Since the number of clauses satisfied is bounded (it's an integer between 0 and m), with high probability it won't take too many tries before you do at least as well as the expectation. How about a deterministic algorithm? Here's a nice way we can do that. First, let's generalize the above statement to talk about general CNF formulas. Claim: Suppose we have a CNF formula of m clauses, with m_1 clauses of size 1, m_2 of size 2, etc. (m = m_1 + m_2 + ...). Then a random assignment satisfies sum_k m_k(1 - 1/2^k) clauses in expectation. Proof: linearity of expectation. Now, here is a deterministic algorithm : Look at x_1: for each of the two possible settings (0 or 1) we can calculate the expected number of clauses satisfied if we were to go with that setting, and then set the rest of the variables randomly. (It is just the number of clauses already satisfied plus sum_k m_k(1-1/2^k), where m_k is the number of clauses of size k in the ``formula to go''.) Fix x_1 to the setting that gives us a larger expectation. Now go on to x_2 and do the same thing, setting it to the value with the highest expectation-to-go, and then x_3 and so on. The point is that E[# clauses satisfied] = (1/2)*E[# satsified | x_1 = 1] + (1/2)*E[# satisfied | x_1 = 0]. So, the larger of the two must be at least as large as the original. This then continues for x_2, x_3, etc. So, our "expectation to go" always increases (or at least never decreases). This is called the ``conditional expectation'' method. The algorithm itself is completely deterministic --- in fact we could rewrite it to get rid of any hint of randomization by just viewing sum_k m_k(1-1/2^k) as a way of weighting the clauses to favor the small ones, but our motivation was based on the randomized method. Interesting fact: getting a 7/8 + epsilon approximation for any constant epsilon (like .001) for MAX-exactly-3-SAT is NP-hard. In general, the area of approximation algorithms and approximation hardness is a major area of algorithms research. Occupies a good fraction of major algorithms conferences.