Combinatorics (Fall 2010)/Duality, Matroid

Duality

Consider the following LP:

[math]\displaystyle{ \begin{align} \text{minimize} && 7x_1+x_2+5x_3\\ \text{subject to} && x_1-x_2+3x_3 &\ge 10\\ && 5x_1-2x_2-x_3 &\ge 6\\ && x_1,x_2,x_3 &\ge 0 \end{align} }[/math]

Let [math]\displaystyle{ OPT }[/math] be the value of the optimal solution. We want to estimate the upper and lower bound of [math]\displaystyle{ OPT }[/math].

Since [math]\displaystyle{ OPT }[/math] is the minimum over the feasible set, every feasible solution forms an upper bound for [math]\displaystyle{ OPT }[/math]. For example [math]\displaystyle{ \boldsymbol{x}=(2,1,3) }[/math] is a feasible solution, thus [math]\displaystyle{ OPT\le 7\cdot 2+1+5\cdot 3=30 }[/math].

For the lower bound, all feasible solution [math]\displaystyle{ \boldsymbol{x} }[/math] satisfies the two constraints:

[math]\displaystyle{ \begin{align} x_1-x_2+3x_3 &\ge 10,\\ 5x_1-2x_2-x_3 &\ge 6.\\ \end{align} }[/math]

Since the [math]\displaystyle{ \boldsymbol{x} }[/math] is restricted to be nonnegative, term-by-term comparison of coefficients shows that

[math]\displaystyle{ 7x_1+x_2+5x_3\ge(x_1-x_2+3x_3)+(5x_1-2x_2-x_3)\ge 16. }[/math]

The idea behind this lower bound process is that we are finding suitable nonnegative multipliers (in the above case the multipliers are all 1s) for the constraints so that when we take their sum, the coefficient of each [math]\displaystyle{ x_i }[/math] in the sum is dominated by the coefficient in the objective function. It is important to ensure that the multipliers are nonnegative, so they do not reverse the direction of the constraint inequality.

To choose the best lower bound, we need to choose the multipliers in such a way that the sum is as large as possible. Interestingly, the problem of finding the best lower bound can be formulated as another LP:

[math]\displaystyle{ \begin{align} \text{maximize} && 10y_1+6y_2\\ \text{subject to} && y_1+5y_2 &\le 7\\ && -y_1+2y_2 &\le 1\\ &&3y_1-y_2 &\le 5\\ && y_1,y_2&\ge 0 \end{align} }[/math]

Here [math]\displaystyle{ y_1 }[/math] and [math]\displaystyle{ y_2 }[/math] were chosen to be nonnegative multipliers for the first and the second constraint, respectively. We call the first LP the primal program and the second LP the dual program. By definition, every feasible solution to the dual program gives a lower bound for the primal program.

Matroid

Matroids

Let [math]\displaystyle{ X }[/math] be a finite set and [math]\displaystyle{ \mathcal{F}\subseteq 2^X }[/math] be a family of subsets of [math]\displaystyle{ X }[/math]. A member set [math]\displaystyle{ S\in\mathcal{F} }[/math] is called maximal if [math]\displaystyle{ S\cup\{x\}\not\in\mathcal{F} }[/math] for any [math]\displaystyle{ x\in X\setminus S }[/math].

For [math]\displaystyle{ Y\subseteq X }[/math], denote [math]\displaystyle{ \mathcal{F}_Y=\{S\in\mathcal{F}\mid S\subseteq Y\} }[/math]. Clearly [math]\displaystyle{ \mathcal{F}_Y }[/math] is the restriction of [math]\displaystyle{ \mathcal{F} }[/math] over [math]\displaystyle{ 2^Y\, }[/math].

Definition

A set system [math]\displaystyle{ \mathcal{F}\subseteq 2^X }[/math] is a matroid if it satisfies: (hereditary) if [math]\displaystyle{ T\subseteq S\in\mathcal{F} }[/math] then [math]\displaystyle{ T\in\mathcal{F} }[/math]; (matroid property) for every [math]\displaystyle{ Y\subseteq X }[/math], all maximal [math]\displaystyle{ S\in\mathcal{F}_Y }[/math] have the same [math]\displaystyle{ |S| }[/math].

Suppose [math]\displaystyle{ \mathcal{F} }[/math] is a matroid. Some matroid terminologies:

• Each member set [math]\displaystyle{ S\in\mathcal{F} }[/math] is called an independent set.
• A maximal independent subset of a set [math]\displaystyle{ Y\subset X }[/math], i.e., a maximal [math]\displaystyle{ S\in\mathcal{F}_Y }[/math], is called a basis of [math]\displaystyle{ Y }[/math].
• The size of the maximal [math]\displaystyle{ S\in\mathcal{F}_Y }[/math] is called the rank of [math]\displaystyle{ Y }[/math], denoted [math]\displaystyle{ r(Y) }[/math].