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*In which we define a multi-commodity flow problem, and we see that its dual is the relaxation of a useful graph partitioning problem. The relaxation can be rounded to yield an approximate graph partitioning algorithm.*

*In which we look at the linear programming formulation of the maximum flow problem, construct its dual, and find a randomized-rounding proof of the max flow – min cut theorem.*

In the first part of the course, we designed approximation algorithms “by hand,” following our combinatorial intuition about the problems. Then we looked at linear programming relaxations of the problems we worked on, and we saw that approximation algorithms for those problems could also be derived by rounding a linear programming solution. We also saw that our algorithms could be interpreted as constructing, at the same time, an integral primal solution and a feasible solution for the dual problem.

Now that we have developed exact combinatorial algorithms for a few problems (maximum flow, minimum s-t cut, global min cut, maximum matching and minimum vertex cover in bipartite graphs), we are going to look at linear programming relaxations of those problems, and use them to gain a deeper understanding of the problems and of our algorithms.

We start with the maximum flow and the minimum cut problems.

*In which we show how to use linear programming to approximate the vertex cover problem.*

**1. Linear Programming Relaxations **

An *integer linear program* (abbreviated ILP) is a linear program (abbreviated LP) with the additional constraints that the variables must take integer values. For example, the following is an ILP:

Where is the set of natural numbers. The advantage of ILPs is that they are a very expressive language to formulate optimization problems, and they can capture in a natural and direct way a large number of combinatorial optimization problems. The disadvantage of ILPs is that they are a very expressive language to formulate combinatorial optimization problems, and finding optimal solutions for ILPs is NP-hard.

If we are interested in designing a polynomial time algorithm (exact or approximate) for a combinatorial optimization problem, formulating the combinatorial optimization problem as an ILP is useful as a first step in the following methodology (the discussion assumes that we are working with a minimization problem):

- Formulate the combinatorial optimization problem as an ILP;
- Derive a LP from the ILP by removing the constraint that the variables have to take integer value. The resulting LP is called a “relaxation” of the original problem. Note that in the LP we are minimizing the same objective function over a larger set of solutions, so ;
- Solve the LP optimally using an efficient algorithm for linear programming;
- If the optimal LP solution has integer values, then it is a solution for the ILP of cost , and so we have found an optimal solution for the ILP and hence an optimal solution for our combinatorial optimization problem;
- If the optimal LP solution has fractional values, but we have a
*rounding*procedure that transforms into an integral solution such that for some constant , then we are able to find a solution to the ILP of cost , and so we have a -approximate algorithm for our combinatorial optimization problem.

In this lecture and in the next one we will see how to round fractional solutions of relaxations of the Vertex Cover and the Set Cover problem, and so we will be able to derive new approximation algorithms for Vertex Cover and Set Cover based on linear programming.

*In which we introduce the theory of duality in linear programming.*

**1. The Dual of Linear Program **

Suppose that we have the following linear program in maximization standard form:

and that an LP-solver has found for us the solution , , , of cost . How can we convince ourselves, or another user, that the solution is indeed optimal, without having to trace the steps of the computation of the algorithm?

Observe that if we have two valid inequalities

then we can deduce that the inequality

(derived by “summing the left hand sides and the right hand sides” of our original inequalities) is also true. In fact, we can also scale the inequalities by a positive multiplicative factor before adding them up, so for every non-negative values we also have

Going back to our linear program (1), we see that if we scale the first inequality by , add the second inequality, and then add the third inequality scaled by , we get that, for every that is feasible for (1),

And so, for every feasible , its cost is

meaning that a solution of cost is indeed optimal.

In general, how do we find a good choice of scaling factors for the inequalities, and what kind of upper bounds can we prove to the optimum?

*In which we introduce linear programming.*

**1. Linear Programming **

A *linear program* is an optimization problem in which we have a collection of variables, which can take real values, and we want to find an assignment of values to the variables that satisfies a given collection of linear inequalities and that maximizes or minimizes a given linear function.

(The term *programming* in *linear programming*, is not used as in *computer programming*, but as in, e.g., *tv programming*, to mean *planning.*)

For example, the following is a linear program.

The linear function that we want to optimize ( in the above example) is called the *objective function*. A *feasible solution* is an assignment of values to the variables that satisfies the inequalities. The value that the objective function gives to an assignment is called the *cost* of the assignment. For example, and is a feasible solution, of cost . Note that if are values that satisfy the inequalities, then, by summing the first two inequalities, we see that

that is,

and so no feasible solution has cost higher than , so the solution , is optimal. As we will see in the next lecture, this trick of summing inequalities to verify the optimality of a solution is part of the very general theory of *duality* of linear programming.

Linear programming is a rather different optimization problem from the ones we have studied so far. Optimization problems such as Vertex Cover, Set Cover, Steiner Tree and TSP are such that, for a given input, there is only a finite number of possible solutions, so it is always trivial to solve the problem in finite time. The number of solutions, however, is typically exponentially big in the size of the input and so, in order to be able to solve the problem on reasonably large inputs, we look for polynomial-time algorithms. In linear programming, however, each variable can take an infinite number of possible values, so it is not even clear that the problem is solvable in finite time.

As we will see, it is indeed possible to solve linear programming problems in finite time, and there are in fact, polynomial time algorithms and efficient algorithms that solve linear programs optimally.

There are at least two reasons why we are going to study linear programming in a course devoted to combinatorial optimization:

- Efficient linear programming solvers are often used as part of the toolkit to design exact or approximate algorithms for combinatorial problems.
- The powerful theory of
*duality*of linear programming, that we will describe in the next lecture, is a very useful mathematical theory to reason about algorithms, including purely combinatorial algorithms for combinatorial problems that seemingly have no connection with continuous optimization.

*In which we describe what this course is about and give two simple examples of approximation algorithms*

**1. Overview **

In this course we study algorithms for combinatorial optimization problems. Those are the type of algorithms that arise in countless applications, from billion-dollar operations to everyday computing task; they are used by airline companies to schedule and price their flights, by large companies to decide what and where to stock in their warehouses, by delivery companies to decide the routes of their delivery trucks, by Netflix to decide which movies to recommend you, by a gps navigator to come up with driving directions and by word-processors to decide where to introduce blank spaces to justify (align on both sides) a paragraph.

In this course we will focus on general and powerful algorithmic techniques, and we will apply them, for the most part, to highly idealized model problems.

Some of the problems that we will study, along with several problems arising in practice, are NP-hard, and so it is unlikely that we can design exact efficient algorithms for them. For such problems, we will study algorithms that are worst-case efficient, but that output solutions that can be sub-optimal. We will be able, however, to prove worst-case bounds to the ratio between the cost of optimal solutions and the cost of the solutions provided by our algorithms. Sub-optimal algorithms with provable guarantees about the quality of their output solutions are called *approximation algorithms*.

The content of the course will be as follows:

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