Documentation

# lsqnonneg

Solve nonnegative linear least-squares problem

## Description

Solve nonnegative least-squares curve fitting problems of the form

example

x = lsqnonneg(C,d) returns the vector x that minimizes norm(C*x-d) subject to x ≥ 0. Arguments C and d must be real.

example

x = lsqnonneg(C,d,options) minimizes with the optimization options specified in the structure options. Use optimset to set these options.

x = lsqnonneg(problem) finds the minimum for problem, where problem is a structure.

example

[x,resnorm,residual] = lsqnonneg(___), for any previous syntax, additionally returns the value of the squared 2-norm of the residual, norm(C*x-d)^2, and returns the residual d-C*x.

[x,resnorm,residual,exitflag,output] = lsqnonneg(___) additionally returns a value exitflag that describes the exit condition of lsqnonneg, and a structure output with information about the optimization process.

example

[x,resnorm,residual,exitflag,output,lambda] = lsqnonneg(___) additionally returns the Lagrange multiplier vector lambda.

## Examples

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Compute a nonnegative solution to a linear least-squares problem, and compare the result to the solution of an unconstrained problem.

Prepare a C matrix and d vector for the problem $\mathrm{min}||Cx-d||$.

C = [0.0372    0.2869
0.6861    0.7071
0.6233    0.6245
0.6344    0.6170];

d = [0.8587
0.1781
0.0747
0.8405];

Compute the constrained and unconstrained solutions.

x = lsqnonneg(C,d)
x = 2×1

0
0.6929

xunc = C\d
xunc = 2×1

-2.5627
3.1108

All entries in x are nonnegative, but some entries in xunc are negative.

Compute the norms of the residuals for the two solutions.

constrained_norm = norm(C*x - d)
constrained_norm = 0.9118
unconstrained_norm = norm(C*xunc - d)
unconstrained_norm = 0.6674

The unconstrained solution has a smaller residual norm because constraints can only increase a residual norm.

Set the Display option to 'final' to see output when lsqnonneg finishes.

Create the options.

options = optimset('Display','final');

Prepare a C matrix and d vector for the problem $\mathrm{min}||Cx-d||$.

C = [0.0372    0.2869
0.6861    0.7071
0.6233    0.6245
0.6344    0.6170];

d = [0.8587
0.1781
0.0747
0.8405];

Call lsqnonneg with the options structure.

x = lsqnonneg(C,d,options);
Optimization terminated.

Call lsqnonneg with outputs to obtain the solution, residual norm, and residual vector.

Prepare a C matrix and d vector for the problem $\mathrm{min}||Cx-d||$.

C = [0.0372    0.2869
0.6861    0.7071
0.6233    0.6245
0.6344    0.6170];

d = [0.8587
0.1781
0.0747
0.8405];

Obtain the solution and residual information.

[x,resnorm,residual] = lsqnonneg(C,d)
x = 2×1

0
0.6929

resnorm = 0.8315
residual = 4×1

0.6599
-0.3119
-0.3580
0.4130

Verify that the returned residual norm is the square of the norm of the returned residual vector.

norm(residual)^2
ans = 0.8315

Request all output arguments to examine the solution and solution process after lsqnonneg finishes.

Prepare a C matrix and d vector for the problem $\mathrm{min}||Cx-d||$.

C = [0.0372    0.2869
0.6861    0.7071
0.6233    0.6245
0.6344    0.6170];

d = [0.8587
0.1781
0.0747
0.8405];

Solve the problem, requesting all output arguments.

[x,resnorm,residual,exitflag,output,lambda] = lsqnonneg(C,d)
x = 2×1

0
0.6929

resnorm = 0.8315
residual = 4×1

0.6599
-0.3119
-0.3580
0.4130

exitflag = 1
output = struct with fields:
iterations: 1
algorithm: 'active-set'
message: 'Optimization terminated.'

lambda = 2×1

-0.1506
-0.0000

exitflag is 1, indicating a correct solution.

x(1) = 0, and the corresponding lambda(1) $\ne$ 0, showing the correct duality. Similarly, x(2) > 0, and the corresponding lambda(2) = 0.

## Input Arguments

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Linear multiplier, specified as a real matrix. Represents the variable C in the problem

For compatibility, the number of rows of C must equal the length of d.

Example: C = [1,2;3,-1;-4,4]

Data Types: double

Additive term, specified as a real vector. Represents the variable d in the problem

For compatibility, the length of d must equal the number of rows of C.

Example: d = [1;-6;5]

Data Types: double

Optimization options, specified as a structure such as optimset returns. You can use optimset to set or change the values of these fields in the options structure. See Set Options for detailed information.

 Display Level of display:'notify' (default) displays output only if the function does not converge.'off' or 'none' displays no output.'final' displays just the final output. TolX Termination tolerance on x, a positive scalar. The default is 10*eps*norm(C,1)*length(C). See Tolerances and Stopping Criteria.

Example: options = optimset('Display','final')

Data Types: struct

Problem structure, specified as a structure with the following fields.

Field NameEntry

C

Real matrix

d

Real vector

solver

'lsqnonneg'

options

Options structure such as returned by optimset

The simplest way to obtain a problem structure is to export the problem from the Optimization app.

Data Types: struct

## Output Arguments

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Solution, returned as a real vector. The length of x is the same as the length of d.

Squared residual norm, returned as a nonnegative scalar. Equal to norm(C*x-d)^2.

Residual, returned as a real vector. The residual is d - C*x.

Reason lsqnonneg stopped, returned as an integer.

 1 Function converged to a solution x. 0 Number of iterations exceeded options.MaxIter.

Information about the optimization process, returned as a structure with fields:

 iterations Number of iterations taken algorithm 'active-set' message Exit message

Lagrange multipliers, returned as a real vector. The entries satisfy the complementarity condition x'*lambda = 0. This means lambda(i) < 0 when x(i) is approximately 0, and lambda(i) is approximately 0 when x(i) > 0.

## Algorithms

lsqnonneg uses the algorithm described in [1]. The algorithm starts with a set of possible basis vectors and computes the associated dual vector lambda. It then selects the basis vector corresponding to the maximum value in lambda to swap it out of the basis in exchange for another possible candidate. This continues until lambda ≤ 0.

## References

[1] Lawson, C. L. and R. J. Hanson. Solving Least-Squares Problems. Upper Saddle River, NJ: Prentice Hall. 1974. Chapter 23, p. 161.