To solve a constrained nonlinear optimization
problem using the Procedural API, you must write a MATLAB function
(called an Evaluator in the terminology of the SDK) that
computes values for the objective and constraints. You can
access a wide range of information about the problem and the current
state of the solution in this Evaluator, and signal whether the SDK
should continue or terminate the solution process early.
function ret = EvaluateCallback(prob, refuser,
evaltype)
var = SolverSDK('VarValue',
prob, 'MyVars');
fcn = [0 0];
fcn(1) = var(1) + var(2); % x1 +
x1 = 1
fcn(2) = var(1) * var(2); % x1 *
x2 >= 0
obj = var(1)*var(1) + var(2)*var(2); %
objective
SolverSDK('FcnValueSet',
prob,
'MyCons', fcn);
SolverSDK('FcnValueSet',
prob,
'MyObj', obj);
ret = 'Continue';
end
You call functions to create a Problem object, and
add this Evaluator to the Problem object's collection of
Evaluators. In this example, we specify constraints with
"right hand sides" and "relations" (<=, =,
>=). The SDK offers several ways to specify constraints --
another way is shown in the Object-Oriented
API example code.
For any type of optimization problem, you simply
call SolverSDK ('SolverOptimize', prob). All errors are
reported by raising exceptions, which will cause the catch
clause to be executed.
rhs = [1 0];
rel = {['EQ'] ['GE']};
try
prob = SolverSDK('Problem');
SolverSDK('ProbTypeSet', prob,
'Minimize', 'OptNLP');
SolverSDK('VariableAdd', prob,
'MyVars', 'Decision', 2);
SolverSDK('FunctionAdd', prob,
'MyCons', 'Constraint', 2);
SolverSDK('FunctionAdd', prob,
'MyObj', 'Objective', 1);
SolverSDK('FcnRelation', prob,
'MyCons', rel, rhs);
SolverSDK('EvaluatorAdd', prob,
'EvaluateCallback',
'', 'Function');
[status, objval] = SolverSDK('SolverOptimize',
prob);
x = SolverSDK('VarFinalValue', prob,
'MyVars');
catch
errstr = lasterror;
disp(errstr.message);
end
if prob ~= 0
SolverSDK('ProbFree', prob);
end
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