Authors: Benjamin Granger, Marta Yu, Kathleen Zhou (ChE 345 Spring 2014)

Optimization with absolute values is a special case of linear programming in which a problem made nonlinear due to the presence of absolute values is solved using linear programming methods.

Absolute value functions themselves are very difficult to perform standard optimization procedures on. They are not continuously differentiable functions, are nonlinear, and are relatively difficult to operate on. However, through simple manipulation of the absolute value expression, these difficulties can be avoided and the problem can be solved using linear programming.

If an absolute value function contains a linear function, then the absolute value can be reformulated into two linear expressions. In other words,

can be reformulated into two linear expressions if the function

. This function is effectively the combination two piecewise functions:

. This methodology is the basis of performing linear programming with absolute values.

. This relation is easiest to see using a number line, as follows:

Figure 1: Number line depicting the above absolute value problem.

This number line represents both the absolute value function as well as the two combined linear functions described above, demonstrating that the two formulations are equivalent.

Absolute values as part of the objective function of a model can also be reformulated to become linear, in certain cases. If the objective is a minimization problem of the form

, then the model can easily be reformulated to be solved using linear programming. If the objective is a minimization problem of the form

, unfortunately, the model cannot be reformulated as a standard LP model, and instead must be solved using mixed-integer linear programming.

In the former two cases, the model can be reformulated by introducing a new variable,

Successive linear programming methods involve generating and solving a sequence of linear programming problems to ultimately solve one absolute value problem. Olvi Mangasarian of the Computer Sciences Department of the University of Wisconsin-Madison worked on an algorithm for solving absolute value problems in this way, described in the paperAbsolute Value Equation Solution via Linear Programming.[3]The algorithm is summarized as follows.

, a tolerance, and a maximum number of iterations. Typical values are approximately 10^-6, 10^-8, and 10, respectively. Note that the algorithm starts at iteration

Begin by solving the following linear program to determine initial primal

Check to see if either of two conditions are true. First, if there are zero components satisfying

. Or second, if the maximum number of iterations has been reached. If either condition occurs, then stop iterating.

Solve the following primal linear program to determine new dual optimal variables

In completing this algorithm, the optimal solution to the original problem will be

is an observation. To minimize the deviation, the problem is formulated in a basic form as:

as the objective function, and linear constraints are

The nonlinearity in this form generates from the absolute value function. However, by substituting for

, the problem can be transformed into a linear problem. In the linearization,

It can be proved that at the optimal solution, we have

. Thus, the problem is transformed into a LP problem, since

is the deviation under the i^th observation and b_j is the j^th parameter in the equation.

that will satisfy the following two inequalities:

. Thus, the problem can be written in the form

We replace the absolute value quantities with a single variable:

We must introduce additional constraints to ensure we do not lose any information by doing this substitution:

The problem has now been reformulated as a linear programming problem that can be solved normally:

The optimum value for the objective function is

1. S. Boyd, L. Vandenberghe,Convex Optimization, Cambridge University Press, 2004.

3. O.L. Mangasarian,Absolute Value Equation Solution via Linear Programming.Retrieved fromftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/13-01.pdf.

4. O.L. Mangasarian,Absolute Value Equation Solution via ConcaveMinimization.Retrieved fromftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/06-02.pdf.

5. T.S. Ferguson,Linear Programming: A Concise Introduction.Retrieved from

6. B.A. McCarl, T.H. Spreen,Linear Programming Modeling: Nonlinearities and Approximation.Retrieved from