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Saturday, July 23, 2016

Coursera Approximation Algorithms Part II - Peer Assignment 1



(1) There are $ m $ variables (i.e. number of subsets) and $ n $ constraints (number of nodes) in the primal program. So there are $ n $ variables and $ m $ constraints in the dual. To fit of the rest of the problem, we name the dual variables $ y_i $.

The primal program is a minimization, so the dual program is a maximization.

The primal program constraints vector is all 1, so the optimization criterion is

$ \min \sum\limits_{1 \le i \le n} y_i $

The constraint is really just a transpose of the constraint matrix, so we have

$ \forall j \in [1, m] \sum\limits_{e_i \in S_j} y_i \le w_j $.

Last but not least, we have the non-negativity constriants

$ \forall i \in [1,n ] y_i \ge 0 $

(2) There are two ways to look at this and they are equally valid.

When we increase the dual variable $ y_i $, the associated subset $ S_i $ is not currently in the cover, and then we add it to the cover, so there is no way we will attempt to increase $ y_i $ again. So there is at most once in the algorithm a dual variable is increased.

The algorithm is maintaining dual feasibility and it increase a dual variable to the limit once we try to increase it, therefore, the dual variable cannot be increased again, so again, we show that a dual variable can be increased at most once.

(3) It is obvious that if the algorithm terminates, it is a solution (because it gets out the while loop).

The algorithm must terminate because there is only a finite number of subsets. Worst case we take them all.

(4) The original primal program is a minimization program, so the optimal primal solution is always better than the optimal primal integral solution, so in other words:

$ OPT \ge val(x*) $

By strong duality, we have $ val(x*) = val(y*) $, so we have

$ OPT \ge val(x*) = val(y*) $

(5) First, the algorithm start with the feasible zero vector.
Next, the algorithm make sure the vector $ y $ stay feasible by making sure it does not break any constraints. Therefore when the algorithm terminates, $ y $ is a dual feasible vector.

(6) Since the dual program is a maximization program, so any the $ val(y) $ for any feasible vector $ y $ will be at most $ val(y*) $, or in equations, we have:

$ OPT \ge val(x*) = val(y*) \ge val(y) $

(7) $ j \in I $ implies that we have, in the course of the algorithm that pick it, max out the constraint, so we have:

$ \sum\limits_{e_i \in S_j} y_i = w_j $

This is of course the same as

$ w_j = \sum\limits_{e_i \in S_j} y_i $

(8) We simply take the sum of the above over all j, that gives

$ \sum\limits_{j \in I} w_j = \sum\limits_{j \in I} \sum\limits_{e_i \in S_j} y_i $

(9) Let's forcefully swap the summation order by using an indicator variable, we have

$ \sum\limits_{j \in I} w_j = \sum\limits_{1 \le i \le n} \sum\limits_{j \in I} X(i, j) y_i $

Where $ X(i, j) = 1 $ if $ e_i \in S_j $ or 0 otherwise.

Now we can factor out the $ y_i $ as that has nothing to do with $ j $.

$ \sum\limits_{j \in I} w_j = \sum\limits_{1 \le i \le n} [y_i \sum\limits_{j \in I} X(i, j)]  $

So how about the $ j $ sum? Remember a node belongs to at most $ f $ sets, so we can write

$ \sum\limits_{j \in I} X(i,j) \le \sum\limits_{j \in [1,m]} X(i,j) \le f $

Therefore we conclude

$ \sum\limits_{j \in I} w_j = \sum\limits_{1 \le i \le n} [y_i \sum\limits_{j \in I} X(i, j)]  \le  \sum\limits_{1 \le i \le n} y_i f =  f\sum\limits_{1 \le i \le n} y_i = f \cdot val(y) $.

(10) Finally, we have in (6)

$ OPT \ge val(x*) = val(y*) \ge val(y) $

Multiplying by $ f $ and using (9), we finally arrive:

$ f OPT \ge f val(x*) = f  \cdot val(y*) \ge f  \cdot val(y) = \sum\limits_{j \in I} w_j $

In other words, the algorithm is a $ f $ approximation of the set cover problem.

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