# Derivative of the solution of a linear program

Let $$x^\star$$ be a solution of the linear program \begin{align} \text{maximize} &\quad c \cdot x \\ \text{subject to} &\quad A \cdot x \leq b \end{align} How can one compute the derivatives of $$x^\star$$ with respect to the linear program parameters? \begin{align} \frac{\partial x^\star}{\partial A} \qquad \frac{\partial x^\star}{\partial b} \qquad \frac{\partial x^\star}{\partial c} \end{align}

where derivatives are interpreted in the matrix calculus sense, e.g. $$\left( \frac{\partial x}{\partial A} \right)_{ijk} = \frac{\partial x_i}{ \partial A_{jk}}$$

I suspect the KKT conditions might be useful here.

• What does it even mean to take the derivative of a vector with a matrix or with another vector? – Math1000 Dec 2 at 20:37
• @Math1000 $\left( \frac{\partial x}{\partial A} \right)_{ijk} = \frac{\partial x_i}{\partial A_{jk}}$ for example. – user76284 Dec 2 at 21:34

This is known as sensitivity analysis. If you have a non-degenerate optimal basic feasible solution, it is relatively simple to find derivatives of the optimal BFS or the optimal objective value with respect to changes in b or c. Changes to A can also be analyzed, but this is somewhat more complicated.

If the optimal BFS is degenerate, these derivatives may not exist.

See just about any textbook on linear programming.

Here's a brief explanation.

First, put your LP into standard form by adding slack variables to eliminate the inequality constraints:

$$\min c^{T}x$$

subject to

$$Ax=b$$

$$x \geq 0$$

Here $$A$$ is a matrix of size $$m$$ by $$n$$ with rank $$m$$.

If there is a unique and non-degenerate optimal basic feasible solution, Then the variables in $$x$$ can be split up into a vector $$x_{B}$$ of $$m$$ basic variables and a vector $$x_{N}$$ of $$n-m$$ nonbasic variables.

Let $$B$$ be the matrix obtaining by taking the columns of $$A$$ that are in the basis and $$A_{N}$$ consist of the remaining columns of $$A$$. Similarly, let $$c_{B}$$ and $$c_{N}$$ be the coefficients in $$c$$ corresponding to basic and nonbasic variables.

You can now write the problem as

$$\min c_{B}^{T}x_{B}+c_{N}^{T}x_{N}$$

subject to

$$Bx_{B}+A_{N}x_{N}=b$$

$$x \geq 0$$.

In the optimal basic solution, we solve for $$x_{B}$$ and write the problem as

$$\min c_{B}^{T}B^{-1}b + (c_{N}^{T}-c_{B}^{T}B^{-1}A_{N})x_{N}$$

subject to

$$x_{B}=B^{-1}b-B^{-1}A_{N}x_{N}$$

$$x \geq 0.$$

An important optimality condition that the simplex method ensures is that

$$r_{N}=c_{N}-c_{B}^{T}B^{-1}A_{N} \geq 0.$$

If the solution is also dual non-degenerate, then $$r_{N}>0$$. We'll need to assume that as well.

In the optimal basic solution, we set all of the variables in $$x_{N}$$ to $$x_{N}^{*}=0$$ and get the values of the basic variables from $$x_{B}^{*}=B^{-1}b$$. By assumption, this optimal basic feasible solution is non-degenerate, meaning that $$B^{-1}b$$ is strictly greater than 0. Small changes to $$b$$ won't change $$r_{N}$$ and won't violate $$x_{B} \geq 0$$, so the solution will remain optimal after small changes to $$b$$.

Now, it should be clear that

$$\frac{\partial x_{B}^{*}}{\partial b}=B^{-1}$$

and

$$\frac{\partial x_{N}^{*}}{\partial b}=0.$$

Small changes in $$c$$ won't change $$x_{B}$$ at all, and will not lose $$r_{N} \geq 0$$. Thus the solution will remain optimal, and $$x_{B}$$ won't change, although the optimal objective value will change. Thus

$$\frac{\partial x_{B}^{*}}{\partial c}=0$$

and

$$\frac{\partial x_{N}^{*}}{\partial c}=0.$$

If the assumptions of non-degeneracy are violated then these derivatives may simply not exist!

In a similar way, you can analyze changes in $$A_{N}$$ or in $$B$$.

This stuff is discussed in V. Chvatal's Linear Programming among many other textbooks on the subject.

• Do you know of a specific reference that addresses partial derivatives of the attained solution with respect to the parameters? I only found ones addressing partial derivatives of the attained value. – user76284 Dec 5 at 3:04
• I've expanded the answer including a reference and showing you how to find derivatives of the optimal solution with respect to changes in $b$ and $c$. – Brian Borchers Dec 5 at 4:26