The OPTQP Procedure

Example 13.1 Linear Least Squares Problem

The linear least squares problem arises in the context of determining a solution to an overdetermined set of linear equations. In practice, these equations could arise in data fitting and estimation problems. An overdetermined system of linear equations can be defined as

$\mathbf{Ax} = \mathbf{b}$

where $\mathbf{A} \in \mathbb {R}^{m \times n}$ , $\mathbf{x}\in \mathbb {R}^ n$ , $\mathbf{b} \in \mathbb {R}^ m$ , and $m > n$ . Since this system usually does not have a solution, you need to be satisfied with some sort of approximate solution. The most widely used approximation is the least squares solution, which minimizes $\| \mathbf{Ax} - \mathbf{b}\| _2^2$ .

This problem is called a least squares problem for the following reason. Let $\mathbf{A}$ , $\mathbf{x}$ , and $\mathbf{b}$ be defined as previously. Let $k_ i(x)$ be the kth component of the vector $\mathbf{Ax} - \mathbf{b}$ :

$k_ i(x) = a_{i1}x_1 + a_{i2}x_2 + \cdots + a_{in}x_ n - b_ i,\; i = 1,2,\ldots , m$

By definition of the Euclidean norm, the objective function can be expressed as follows:

$\| \mathbf{Ax} - \mathbf{b}\| _2^2 = \sum \limits _{i = 1}^ m k_ i(x)^2$

Therefore, the function you minimize is the sum of squares of m terms $k_ i(x)$ ; hence the term least squares. The following example is an illustration of the linear least squares problem; that is, each of the terms $k_ i$ is a linear function of x.function $\sum _{ij}a_{ij}x_ j$ plus a constant, $-b_ i$ .

Consider the following least squares problem defined by

$\mathbf{A} = \left[\begin{array}{rc} 4 & 0 \\ -1 & 1 \\ 3 & 2 \end{array}\right],\quad \mathbf{b} = \left[\begin{array}{c} 1 \\ 0 \\ 1 \end{array}\right]$

This translates to the following set of linear equations:

$4x_1 = 1, \quad -x_1 + x_2 = 0, \quad 3x_1 + 2x_2 = 1$

The corresponding least squares problem is

$\mbox{minimize} \quad (4x_1 -1)^2 + (-x_1 + x_2)^2 + (3x_1 + 2x_2 -1)^2$

The preceding objective function can be expanded to

$\mbox{minimize} \quad 26x_1^2 + 5x_2^2 + 10 x_1x_2 - 14 x_1 -4x_2 + 2$

In addition, you impose the following constraint so that the equation $3x_1 + 2x_2 = 1$ is satisfied within a tolerance of $0.1$ :

$0.9 \leq 3x_1 + 2x_2 \leq 1.1$

You can create the QPS-format input data set by using the following SAS statements:

data lsdata;
   input field1 $ field2 $ field3 $ field4 field5 $ field6 @;
   datalines;
NAME     .      LEASTSQ     .            .         .
ROWS     .      .           .            .         .
N        OBJ    .           .            .         .
G        EQ3    .           .            .         .
COLUMNS  .      .           .            .         .
.        X1     OBJ        -14           EQ3       3
.        X2     OBJ        -4            EQ3       2
RHS      .      .           .            .         .
.        RHS    OBJ        -2            EQ3       0.9
RANGES   .      .           .            .         .
.        RNG    EQ3         0.2          .         .
BOUNDS   .      .           .            .         .
FR       BND1   X1          .            .         .
FR       BND1   X2          .            .         .
QUADOBJ  .      .           .            .         .
.        X1     X1          52           .         .
.        X1     X2          10           .         .
.        X2     X2          10           .         .
ENDATA   .      .           .            .         .
;

The decision variables $x_1$ and $x_2$ are free, so they have bound type FR in the BOUNDS section of the QPS-format data set.

You can use the following SAS statements to solve the least squares problem:

proc optqp data=lsdata
   printlevel = 0
   primalout = lspout;
run;

The optimal solution is displayed in Output 13.1.1.

Output 13.1.1: Solution to the Least Squares Problem

Primal Solution

Obs	Objective Function ID	RHS ID	Variable Name	Variable Type	Linear Objective Coefficient	Lower Bound	Upper Bound	Variable Value	Variable Status
1	OBJ	RHS	X1	F	-14	-1.7977E308	1.7977E308	0.23810	O
2	OBJ	RHS	X2	F	-4	-1.7977E308	1.7977E308	0.16190	O

The iteration log is shown in Output 13.1.2.

Output 13.1.2: Iteration Log

NOTE: The problem LEASTSQ has 2 variables (2 free, 0 fixed).

NOTE: The problem has 1 constraints (0 LE, 0 EQ, 0 GE, 1 range).

NOTE: The problem has 2 constraint coefficients.

NOTE: The objective function has 2 Hessian diagonal elements and 1 Hessian

elements above the diagonal.

NOTE: The QP presolver value AUTOMATIC is applied.

NOTE: The QP presolver removed 0 variables and 0 constraints.

NOTE: The QP presolver removed 0 constraint coefficients.

NOTE: The presolved problem has 2 variables, 1 constraints, and 2 constraint

coefficients.

NOTE: The QP solver is called.

NOTE: The Interior Point algorithm is used.

NOTE: The deterministic parallel mode is enabled.

NOTE: The Interior Point algorithm is using up to 4 threads.

Primal Bound Dual

Iter Complement Duality Gap Infeas Infeas Infeas

0 1.91812E-02 5.89357E-03 1.96367E-08 0.00000E+00 3.53901E-04

1 9.04861E-04 2.83115E-04 7.25763E-10 0.00000E+00 1.30555E-05

2 1.53700E-05 4.94409E-06 7.77903E-12 0.00000E+00 1.30555E-07

3 1.53570E-07 4.93971E-08 8.81735E-14 0.00000E+00 1.30562E-09

NOTE: Optimal.

NOTE: Objective = 0.0095238095.

NOTE: The Interior Point solve time is 0.00 seconds.

NOTE: The data set WORK.LSPOUT has 2 observations and 9 variables.