Scribed by Rachel Lawrence

*In which we introduce semidefinite programming and apply it to Max Cut.*

**1. Overview **

We begin with an introduction to Semidefinite Programming (SDP). We will then see that, using SDP, we can find a cut with the same kind of near-optimal performance for Max Cut in random graphs as we got from the greedy algorithm — that is,

in random graphs . More generally, we will prove that you can always find a cut at least this large in the case that G is triangle-free and with maximum vertex degree , which will imply the bound in random graphs. We will also see how to use SDP to certify an upper bound:

with high probability in

Methods using SDP will become particularly helpful in future lectures when we consider planted-solution models instead of fully random graphs: greedy algorithms will fail on some analogous problems where methods using SDP can succeed.

**2. Semidefinite Programming **

Semidefinite Programming (SDP) is a form of convex optimization, similar to linear programming but with the addition of a constraint stating that, if the variables in the linear program are considered as entries in a matrix, that matrix is positive semidefinite. To formalize this, we begin by recalling some basic facts from linear algebra.

** 2.1. Linear algebra review **

Definition 1 (Positive Semidefinite)A matrix is positive semidefinite (abbreviated PSD and written ) if it is symmetric and all its eigenvalues are non-negative.

We will also make use of the following facts from linear algebra:

- If is a symmetric matrix, then all the eigenvalues of are real, and, if we call the eigenvalues of with repetition, we have
where the are orthonormal eigenvectors of the .

- The smallest eigenvalue of has the characterization
and the optimization problem in the right-hand side is solvable up to arbitrarily good accuracy

This gives us the following lemmas:

Lemma 2if and only if for every vector we have .

*Proof:* From part (2) above, the smallest eigenvalue of M is given by

Noting that we always have , then if and only if the numerator on the right is always non-negative.

Lemma 3If , then

*Proof:* , . By Lemma 2, this implies .

Lemma 4If and , then

*Proof:* , . By Lemma 2, this implies .

** 2.2. Formulation of SDP **

With these characterizations in mind, we define a *semidefinite program* as an optimization program in which we have real variables , with , and we want to maximize, or minimize, a linear function of the variables such that linear constraints over the variables are satisfied (so far this is the same as a linear program) and subject to the additional constraint that the matrix is PSD. Thus, a typical semidefinite program (SDP) looks like

where the matrices and the scalars are given, and the entries of are the variables over which we are optimizing.

We will also use the following alternative characterization of PSD matrices

Lemma 5A matrix is PSD if and only if there is a collection of vectors such that, for every , we have .

*Proof:* Suppose that and are such that for all and . Then is PSD because for every vector we have

Conversely, if is PSD and we write it as

we have

and we see that we can define vectors by setting

and we do have the property that

This leads to the following equivalent formulation of the SDP optimization problem:

where our variables are vectors . This is the statement of the optimization problem that we will most commonly use.

** 2.3. Polynomial time solvability **

From lemmas 3 and 4, we recall that if and are two matrices such that and , and if is a scalar, then and . This means that the set of PSD matrices is a convex subset of , and that the above optimization problem is a convex problem.

Using the ellipsoid algorithm, one can solve in polynomial time (up to arbitrarily good accuracy) any optimization problem in which one wants to optimize a linear function over a convex feasible region, provided that one has a *separation oracle* for the feasible region: that is, an algorithm that, given a point,

- Checks whether it is feasible and, if not,
- Constructs an inequality that is satisfied by all feasible point but not satisfied by the given point.

In order to construct a separation oracle for a SDP, it is enough to solve the following problem: given a matrix , decide if it is PSD or not and, if not, construct an inequality that is satisfied by the entries of all PSD matrices but that is not satisfied by . In order to do so, recall that the smallest eigenvalue of is

and that the above minimization problem is solvable in polynomial time (up to arbitrarily good accuracy). If the above optimization problem has a non-negative optimum, then is PSD. If it is a negative optimum , then the matrix is not PSD, and the inequality

is satisfied for all PSD matrices but fails for . Thus we have a separation oracle and we can solve SDPs in polynomial time up to arbitrarily good accuracy.

**3. SDP Relaxation of Max Cut and Random Hyperplane Rounding **

The Max Cut problem in a given graph has the following equivalent characterization, as a quadratic optimization problem over real variables , where :

We can interpret this as associating every vertex with a value , so that the cut edges are those with one vertex of value and one of value .

While quadratic optimization is NP-hard, we can instead use a relaxation to a polynomial-time solvable problem. We note that any quadratic optimization problem has a natural relaxation to an SDP, in which we relax real variables to take vector values and we change multiplication to inner product:

Figure 1: The hyperplane through the origin defines a cut partitioning the vertices into sets and .

Solving the above SDP, which is doable in polynomial time up to arbitrarily good accuracy, gives us a unit vector for each vertex . A simple way to convert this collection to a cut is to take a random hyperplane through the origin, and then define to be the set of vertices such that is above the hyperplane. Equivalently, we pick a random vector according to a rotation-invariant distribution, for example a Gaussian distribution, and let be the set of vertices such that .

Let be an edge: One sees that if is the angle between and , then the probability is cut is proportional to :

and the contribution of to the cost function is

Some calculus shows that for every we have

and so

so we have a polynomial time approximation algorithm with worst-case approximation guarantee .

Next time, we will see how the SDP relaxation behaves on random graphs, but first let us how it behaves on a large class of graphs.

**4. Max Cut in Bounded-Degree Triangle-Free Graphs **

Theorem 6If is a triangle-free graph in which every vertex has degree at most , then

*Proof:* Consider the following feasible solution for the SDP: we associate to each node an -dimensional vector such that , if , and otherwise. We immediately see that for every and so the solution is feasible.

For example, if we have a graph such that vertex 1 is adjacent to vertices 3 and 5:

2 | 3 | 4 | 5 | |||

0 | 0 | |||||

0 | 0 | 0 | 0 | |||

0 | 0 | 0 | ||||

0 | 0 | 0 | 0 | 0 |

Let us transform this SDP solution into a cut using a random hyperplane.

We see that, for every edge we have

The probability that is cut by is

and

so that the expected number of cut edges is at least .

In section 3, the SDP relaxation (of the QP) has an *equality* sign in its constraints. Shouldn’t it be an inequality sign?