1. Introduction
The linearization of nonlinear systems is an efficient tool for finding approximate solutions and treatment analysis of these systems, especially in application [1] -[3] . Some researchers have used some methods based on the optimization problem [4] . But in many applications for nonlinear and nonsmooth functions, we are faced to some problems. In fact, piecewise linearization is a more efficient tool for finding approximate solutions. Some researchers have used piecewise linearization in applications [5] [6] . Also, some researchers have used piecewise linearization to solve ODEs and PDEs [7] .
First, we consider a nonlinear function. Let
be a nonlinear function. We suppose that
varies in a subset of
as
and this subset is compact. Our aim is to approximate the
nonlinear function
by a piecewise linear function as follows:
(1)
where
is
th subset in partitioning of
as
. As we know, this partitioning has bel-
low properties:
1) ![](//html.scirp.org/file/4-7402402x16.png)
2) ![](//html.scirp.org/file/4-7402402x17.png)
Also,
is a characteristic function on
such that:
(2)
Now, let
. As we know
is a Hilbert space of
with the follow-
ing inner product:
(3)
and
(4)
Definition 1. We define
be the set of all
of the form (1).
Definition 2. If
is a nonlinear function and
, we define
as follows:
(5)
Theorem. The subset
is dens on
.
Proof. Suppose that
be a nonlinear function that
.
![]()
Definition 3. We call
the best piecewise linear approximation of
if for any ![]()
we have
.
In fact, by above definition
is optimal solution of the following optimization problem:
(6)
Obviously, because
, the optimization problem has optimal solution.
2. Approach
At first, we consider a nonlinear function
. Secondly, we explain this approach for a nonlinear function
. Then, we explain this approach for a nonlinear function
.
1) Let to consider the bellow optimization problem
![]()
where,
is a nonlinear function and
. As we know
can be replaced by
.
Now, we decompose interval
to
subintervals
(See Figure 1).
Since,
, we have
(7)
Our objective function is a functional. Now, we reduce this functional to a summation as follows:
(8)
So, the optimization problem (8) is as follows:
(9)
But, the optimization problem (9) is a nonlinear programming problem. We reduce this problem to a linear programming problem by relation
such that
. So, our optimization problem will be as follows:
(10)
2) Second, we consider a nonlinear function
. So, we have the optimization problem as follows:
(11)
where
is the ith partition in partitioning of
and
. Also we can replace
by
. As, we explained in 1) the optimization problem (11) will be reduced to a linear programming problem as follows:
(12)
where
and
are numbers of subintervals on axises
and
, respectively (See Figure 2).
3) Third, we consider a nonlinear function
. So, we have the optimization problem as fol-
lows:
(13)
As, we explained in sections 1) and 2) this optimization problem will be reduced to a linear programming problem as follows:
(14)
where
are numbers of subintervals on axises
respectively.
3. Examples
In this section, we show efficiency of our approach by several examples. Also, we define the root mean squared error by follow relation:
(15)
Example 1. We consider nonlinear nonsmooth function
on interval
.
As we explained in section 1), the linear programming corresponding to this function is as follows:
(16)
The optimal solution of linear programming problem (16) is the best piecewise linearization of the function
on
. We let
and
, respectively (See Figure 3, Figure 4). In this example, we have
for
. As we can see the approximate piecewise linearization of this function is high accurate.
Example 2. We consider nonlinear function
on interval
. We have obtained the piecewise approximation of this nonlinear function using two other methods. These methods are Splines Piecewise Approximation (SPA) and Mixture of Polynomials (MOP). Then we have compared these with our method.
As we explained in section 1), the linear programming corresponding to this function is as follows:
(17)
![]()
Figure 3. The figure of piecewise function approximation of nonlinear function
for
.
![]()
Figure 4. The figure of piecewise function approximation of nonlinear function
for
.
The optimal solution of linear programming problem (17) is the best piecewise linearization of the function
on
. We let
(See Figure 5). In this example, we have
while
for Splines Piecewise Approximation and
. As we can see the approxi- mate piecewise linearization of this function using our method is more accurate in compared with two other methods.
Example 3. We consider nonlinear non smooth function
on
.
As we explained in section 2), the linear programming corresponding to this function is as follows:
(18)
The optimal solution of linear programming problem (18) is the best piecewise linearization of the function
on
. We let
and
(See Figure 6, Figure 7). In this example,
![]()
Figure 5. The figure of piecewise function approximation of nonlinear function
for
.
![]()
Figure 6. The figure of piecewise function approximation of nonlinear function
for
.
![]()
Figure 7. The figure of piecewise function approximation of nonlinear function
for
.
we have
and
, respectively. As we can see the approximate piecewise linearization of this function is high accurate.
4. Conclusion
Our method for piecewise linearization of nonlinear functions is extensible to
by the function
. As we can see, this approximation is high accurate in comparison of other methods and this method is very simple for achieving this optimal solution. Also, this piecewise linearization form of nonlinear functions is useful for many applications, especially for nonlinear nonsmooth optimization, nonlinear differential equations, fuzzy ODE and PDE differential equations and so on.
NOTES
*Corresponding author.