Accelerating the pace of engineering and science

# fgoalattain

Solve multiobjective goal attainment problems

## Equation

Finds the minimum of a problem specified by

weight, goal, b, and beq are vectors, A and Aeq are matrices, and c(x), ceq(x), and F(x) are functions that return vectors. F(x), c(x), and ceq(x) can be nonlinear functions.

x, lb, and ub can be passed as vectors or matrices; see Matrix Arguments.

## Syntax

x = fgoalattain(fun,x0,goal,weight)
x = fgoalattain(fun,x0,goal,weight,A,b)
x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq)
x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub)
x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub,nonlcon)
x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub,nonlcon,options)
x = fgoalattain(problem)
[x,fval] = fgoalattain(...)
[x,fval,attainfactor] = fgoalattain(...)
[x,fval,attainfactor,exitflag] = fgoalattain(...)
[x,fval,attainfactor,exitflag,output] = fgoalattain(...)
[x,fval,attainfactor,exitflag,output,lambda] = fgoalattain(...)

## Description

fgoalattain solves the goal attainment problem, which is one formulation for minimizing a multiobjective optimization problem.

 Note:   Passing Extra Parameters explains how to pass extra parameters to the objective functions and nonlinear constraint functions, if necessary.

x = fgoalattain(fun,x0,goal,weight) tries to make the objective functions supplied by fun attain the goals specified by goal by varying x, starting at x0, with weight specified by weight.

x = fgoalattain(fun,x0,goal,weight,A,b) solves the goal attainment problem subject to the linear inequalities A*x ≤ b.

x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq) solves the goal attainment problem subject to the linear equalities Aeq*x = beq as well. Set A = [] and b = [] if no inequalities exist.

x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub) defines a set of lower and upper bounds on the design variables in x, so that the solution is always in the range lb ≤ x ≤ ub.

 Note:   See Iterations Can Violate Constraints.

x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub,nonlcon) subjects the goal attainment problem to the nonlinear inequalities c(x) or nonlinear equality constraints ceq(x) defined in nonlcon. fgoalattain optimizes such that c(x) ≤ 0 and ceq(x) = 0. Set lb = [] and/or ub = [] if no bounds exist.

x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub,nonlcon,options) minimizes with the optimization options specified in options. Use optimoptions to set these options.

x = fgoalattain(problem) finds the minimum for problem, where problem is a structure described in Input Arguments.

Create the problem structure by exporting a problem from Optimization app, as described in Exporting Your Work.

[x,fval] = fgoalattain(...) returns the values of the objective functions computed in fun at the solution x.

[x,fval,attainfactor] = fgoalattain(...) returns the attainment factor at the solution x.

[x,fval,attainfactor,exitflag] = fgoalattain(...) returns a value exitflag that describes the exit condition of fgoalattain.

[x,fval,attainfactor,exitflag,output] = fgoalattain(...) returns a structure output that contains information about the optimization.

[x,fval,attainfactor,exitflag,output,lambda] = fgoalattain(...) returns a structure lambda whose fields contain the Lagrange multipliers at the solution x.

 Note:   If the specified input bounds for a problem are inconsistent, the output x is x0 and the output fval is [].

## Input Arguments

Function Arguments contains general descriptions of arguments passed into fgoalattain. This section provides function-specific details for fun, goal, nonlcon, options, weight, and problem:

fun

The function to be minimized. fun is a function that accepts a vector x and returns a vector F, the objective functions evaluated at x. The function fun can be specified as a function handle for a function file:

`x = fgoalattain(@myfun,x0,goal,weight)`

where myfun is a MATLAB® function such as

```function F = myfun(x)
F = ...         % Compute function values at x.```

fun can also be a function handle for an anonymous function.

`x = fgoalattain(@(x)sin(x.*x),x0,goal,weight);`

If the user-defined values for x and F are matrices, they are converted to a vector using linear indexing.

To make an objective function as near as possible to a goal value, (i.e., neither greater than nor less than) use optimoptions to set the GoalsExactAchieve option to the number of objectives required to be in the neighborhood of the goal values. Such objectives must be partitioned into the first elements of the vector F returned by fun.

If the gradient of the objective function can also be computed and the GradObj option is 'on', as set by

`options = optimoptions('fgoalattain','GradObj','on')`

then the function fun must return, in the second output argument, the gradient value G, a matrix, at x. The gradient consists of the partial derivative dF/dx of each F at the point x. If F is a vector of length m and x has length n, where n is the length of x0, then the gradient G of F(x) is an n-by-m matrix where G(i,j) is the partial derivative of F(j) with respect to x(i) (i.e., the jth column of G is the gradient of the jth objective function F(j)).

 Note:   Setting GradObj to 'on' is effective only when there is no nonlinear constraint, or when the nonlinear constraint has GradConstr set to 'on' as well. This is because internally the objective is folded into the constraints, so the solver needs both gradients (objective and constraint) supplied in order to avoid estimating a gradient.

goal

Vector of values that the objectives attempt to attain. The vector is the same length as the number of objectives F returned by fun. fgoalattain attempts to minimize the values in the vector F to attain the goal values given by goal.

nonlcon

The function that computes the nonlinear inequality constraints c(x) ≤ 0 and the nonlinear equality constraints ceq(x) = 0. The function nonlcon accepts a vector x and returns two vectors c and ceq. The vector c contains the nonlinear inequalities evaluated at x, and ceq contains the nonlinear equalities evaluated at x. The function nonlcon can be specified as a function handle.

```x = fgoalattain(@myfun,x0,goal,weight,A,b,Aeq,beq,...
lb,ub,@mycon)```

where mycon is a MATLAB function such as

```function [c,ceq] = mycon(x)
c = ...         % compute nonlinear inequalities at x.
ceq = ...       % compute nonlinear equalities at x.```

If the gradients of the constraints can also be computed and the GradConstr option is 'on', as set by

`options = optimoptions('fgoalattain','GradConstr','on')`

then the function nonlcon must also return, in the third and fourth output arguments, GC, the gradient of c(x), and GCeq, the gradient of ceq(x). Nonlinear Constraints explains how to "conditionalize" the gradients for use in solvers that do not accept supplied gradients.

If nonlcon returns a vector c of m components and x has length n, where n is the length of x0, then the gradient GC of c(x) is an n-by-m matrix, where GC(i,j) is the partial derivative of c(j) with respect to x(i) (i.e., the jth column of GC is the gradient of the jth inequality constraint c(j)). Likewise, if ceq has p components, the gradient GCeq of ceq(x) is an n-by-p matrix, where GCeq(i,j) is the partial derivative of ceq(j) with respect to x(i) (i.e., the jth column of GCeq is the gradient of the jth equality constraint ceq(j)).

 Note:   Setting GradConstr to 'on' is effective only when GradObj is set to 'on' as well. This is because internally the objective is folded into the constraint, so the solver needs both gradients (objective and constraint) supplied in order to avoid estimating a gradient.
 Note   Because Optimization Toolbox™ functions only accept inputs of type double, user-supplied objective and nonlinear constraint functions must return outputs of type double.

Passing Extra Parameters explains how to parameterize the nonlinear constraint function nonlcon, if necessary.

options

Options provides the function-specific details for the options values.

weight

A weighting vector to control the relative underattainment or overattainment of the objectives in fgoalattain. When the values of goal are all nonzero, to ensure the same percentage of under- or overattainment of the active objectives, set the weighting function to abs(goal). (The active objectives are the set of objectives that are barriers to further improvement of the goals at the solution.)

 Note   Setting a component of the weight vector to zero will cause the corresponding goal constraint to be treated as a hard constraint rather than as a goal constraint. An alternative method to set a hard constraint is to use the input argument nonlcon.

When the weighting function weight is positive, fgoalattain attempts to make the objectives less than the goal values. To make the objective functions greater than the goal values, set weight to be negative rather than positive. To make an objective function as near as possible to a goal value, use the GoalsExactAchieve option and put that objective as the first element of the vector returned by fun (see the preceding description of fun and options).

problem

objective

Vector of objective functions

x0

Initial point for x

goal

Goals to attain

weight

Relative importance factors of goals

Aineq

Matrix for linear inequality constraints

bineq

Vector for linear inequality constraints

Aeq

Matrix for linear equality constraints

beq

Vector for linear equality constraints

lb

Vector of lower bounds

ub

Vector of upper bounds

nonlcon

Nonlinear constraint function

solver

'fgoalattain'

options

Options created with optimoptions

## Output Arguments

Function Arguments contains general descriptions of arguments returned by fgoalattain. This section provides function-specific details for attainfactor, exitflag, lambda, and output:

 attainfactor The amount of over- or underachievement of the goals. attainfactor contains the value of γ at the solution. If attainfactor is negative, the goals have been overachieved; if attainfactor is positive, the goals have been underachieved. exitflag Integer identifying the reason the algorithm terminated. The following lists the values of exitflag and the corresponding reasons the algorithm terminated. 1 Function converged to a solutions x. 4 Magnitude of the search direction less than the specified tolerance and constraint violation less than options.TolCon 5 Magnitude of directional derivative less than the specified tolerance and constraint violation less than options.TolCon 0 Number of iterations exceeded options.MaxIter or number of function evaluations exceeded options.MaxFunEvals -1 Stopped by an output function or plot function. -2 No feasible point was found. lambda Structure containing the Lagrange multipliers at the solution x (separated by constraint type). The fields of the structure are lower Lower bounds lb upper Upper bounds ub ineqlin Linear inequalities eqlin Linear equalities ineqnonlin Nonlinear inequalities eqnonlin Nonlinear equalities output Structure containing information about the optimization. The fields of the structure are iterations Number of iterations taken funcCount Number of function evaluations lssteplength Size of final line search step relative to search direction stepsize Final displacement in x algorithm Optimization algorithm used firstorderopt Measure of first-order optimality constrviolation Maximum of constraint functions message Exit message

## Options

Optimization options used by fgoalattain. Use optimoptions to set or change options. See Optimization Options Reference for detailed information.

## Examples

Consider a linear system of differential equations.

An output feedback controller, K, is designed producing a closed loop system

$\begin{array}{c}\stackrel{˙}{x}=\left(A+BKC\right)x+Bu,\\ y=Cx.\end{array}$

The eigenvalues of the closed loop system are determined from the matrices A, B, C, and K using the command eig(A+B*K*C). Closed loop eigenvalues must lie on the real axis in the complex plane to the left of the points [-5,-3,-1]. In order not to saturate the inputs, no element in K can be greater than 4 or be less than -4.

The system is a two-input, two-output, open loop, unstable system, with state-space matrices.

The set of goal values for the closed loop eigenvalues is initialized as

`goal = [-5,-3,-1];`

To ensure the same percentage of under- or overattainment in the active objectives at the solution, the weighting matrix, weight, is set to abs(goal).

Starting with a controller, K = [-1,-1; -1,-1], first write a function file, eigfun.m.

```function F = eigfun(K,A,B,C)
F = sort(eig(A+B*K*C)); % Evaluate objectives```

Next, enter system matrices and invoke an optimization routine.

```A = [-0.5 0 0; 0 -2 10; 0 1 -2];
B = [1 0; -2 2; 0 1];
C = [1 0 0; 0 0 1];
K0 = [-1 -1; -1 -1];       % Initialize controller matrix
goal = [-5 -3 -1];         % Set goal values for the eigenvalues
weight = abs(goal);        % Set weight for same percentage
lb = -4*ones(size(K0));    % Set lower bounds on the controller
ub = 4*ones(size(K0));     % Set upper bounds on the controller
options = optimoptions('fgoalattain','Display','iter');   % Set display parameter
[K,fval,attainfactor] = fgoalattain(@(K)eigfun(K,A,B,C),...
K0,goal,weight,[],[],[],[],lb,ub,[],options)```

You can run this example by using the script goaldemogoaldemo. (From the MATLAB Help browser or the MathWorks Web site documentation, you can click the goaldemo name to display the example.) After about 11 iterations, a solution is

```Active inequalities (to within options.TolCon = 1e-006):
lower      upper     ineqlin   ineqnonlin
1                                1
2                                2
4

K =
-4.0000    -0.2564
-4.0000    -4.0000

fval =
-6.9313
-4.1588
-1.4099

attainfactor =
-0.3863```

### Discussion

The attainment factor indicates that each of the objectives has been overachieved by at least 38.63% over the original design goals. The active constraints, in this case constraints 1 and 2, are the objectives that are barriers to further improvement and for which the percentage of overattainment is met exactly. Three of the lower bound constraints are also active.

In the preceding design, the optimizer tries to make the objectives less than the goals. For a worst-case problem where the objectives must be as near to the goals as possible, use optimoptions to set the GoalsExactAchieve option to the number of objectives for which this is required.

Consider the preceding problem when you want all the eigenvalues to be equal to the goal values. A solution to this problem is found by invoking fgoalattain with the GoalsExactAchieve option set to 3.

```options = optimoptions('fgoalattain','GoalsExactAchieve',3);
[K,fval,attainfactor] = fgoalattain(...
@(K)eigfun(K,A,B,C),K0,goal,weight,[],[],[],[],lb,ub,[],...
options);```

After about seven iterations, a solution is

```K,fval,attainfactor

K =
-1.5954    1.2040
-0.4201   -2.9046

fval =
-5.0000
-3.0000
-1.0000

attainfactor =
1.1304e-022```

In this case the optimizer has tried to match the objectives to the goals. The attainment factor (of 1.1304e-22 or so, depending on your system) indicates that the goals have been matched almost exactly.

## Notes

This problem has discontinuities when the eigenvalues become complex; this explains why the convergence is slow. Although the underlying methods assume the functions are continuous, the method is able to make steps toward the solution because the discontinuities do not occur at the solution point. When the objectives and goals are complex, fgoalattain tries to achieve the goals in a least-squares sense.

## Limitations

The objectives must be continuous. fgoalattain might give only local solutions.

expand all

### Algorithms

Multiobjective optimization concerns the minimization of a set of objectives simultaneously. One formulation for this problem, and implemented in fgoalattain, is the goal attainment problem of Gembicki [3]. This entails the construction of a set of goal values for the objective functions. Multiobjective optimization is discussed in Multiobjective Optimization Algorithms.

In this implementation, the slack variable γ is used as a dummy argument to minimize the vector of objectives F(x) simultaneously; goal is a set of values that the objectives attain. Generally, prior to the optimization, it is not known whether the objectives will reach the goals (under attainment) or be minimized less than the goals (overattainment). A weighting vector, weight, controls the relative underattainment or overattainment of the objectives.

fgoalattain uses a sequential quadratic programming (SQP) method, which is described in Sequential Quadratic Programming (SQP). Modifications are made to the line search and Hessian. In the line search an exact merit function (see [1] and [4]) is used together with the merit function proposed by [5] and [6]. The line search is terminated when either merit function shows improvement. A modified Hessian, which takes advantage of the special structure of the problem, is also used (see [1] and [4]). A full description of the modifications used is found in Goal Attainment Method in "Introduction to Algorithms." Setting the MeritFunction option to 'singleobj' with

```options = optimoptions(options,'MeritFunction','singleobj')
```

uses the merit function and Hessian used in fmincon.

See also SQP Implementation for more details on the algorithm used and the types of procedures displayed under the Procedures heading when the Display option is set to 'iter'.

## References

[1] Brayton, R.K., S.W. Director, G.D. Hachtel, and L.Vidigal, "A New Algorithm for Statistical Circuit Design Based on Quasi-Newton Methods and Function Splitting," IEEE Transactions on Circuits and Systems, Vol. CAS-26, pp 784-794, Sept. 1979.

[2] Fleming, P.J. and A.P. Pashkevich, Computer Aided Control System Design Using a Multi-Objective Optimisation Approach, Control 1985 Conference, Cambridge, UK, pp. 174-179.

[3] Gembicki, F.W., "Vector Optimization for Control with Performance and Parameter Sensitivity Indices," Ph.D. Dissertation, Case Western Reserve Univ., Cleveland, OH, 1974.

[4] Grace, A.C.W., "Computer-Aided Control System Design Using Optimization Techniques," Ph.D. Thesis, University of Wales, Bangor, Gwynedd, UK, 1989.

[5] Han, S.P., "A Globally Convergent Method For Nonlinear Programming," Journal of Optimization Theory and Applications, Vol. 22, p. 297, 1977.

[6] Powell, M.J.D., "A Fast Algorithm for Nonlinear Constrained Optimization Calculations," Numerical Analysis, ed. G.A. Watson, Lecture Notes in Mathematics, Vol. 630, Springer Verlag, 1978.