# I use fcn2optimexpr,but I got an error for scalar optimizationexpression

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sogol bandekian 2022 年 5 月 6 日

rng default
A = [2,3];
a = [-0.9,-2.9];
tdata = 3*rand(200,1);
tdata = sort(tdata); % Increasing times for easier plotting
noisydata=0.07*randn(size(tdata))
ydata = A.*exp(a(1).*tdata).*cos(a(2).*tdata) + noisydata ;
plot(tdata,ydata,'b*')
xlabel 't'
ylabel 'Response'
A = optimvar('A',2);
a = optimvar('a',2);
objfunction =@(A,a)A.*exp(a(1).*tdata).*cos(a(2).*tdata);
response = fcn2optimexpr(objfunction,A,a,'OutputSize',[200,2],"ReuseEvaluation",true)
obj = sum((response - ydata).^2);
%%%%%%%%error appear when run the optimproblem
lsqprob = optimproblem;
lsqprob.Objective=obj
x0.A = [1/2,3/2];
x0.a = [-1/2,-3/2];
show(lsqprob)
[sol ,fval] = solve(lsqprob,x0)
I gor error for optimproblem -->Objective must be a scalar OptimizationExpression or a struct containing a scalar OptimizationExpression.
another problem is that solve can not call

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### 採用された回答

Alan Weiss 2022 年 5 月 8 日
You had a misunderstanding. The A variable should be a scalar, not a vector. All issues stemmed from that one misunderstanding.
rng default
A = 2; % Was [2,3]
a = [-0.9,-2.9];
tdata = 3*rand(200,1);
tdata = sort(tdata); % Increasing times for easier plotting
noisydata=0.07*randn(size(tdata));
ydata = A.*exp(a(1).*tdata).*cos(a(2).*tdata) + noisydata ;
plot(tdata,ydata,'b*')
xlabel 't'
ylabel 'Response'
A = optimvar('A'); % used to be of size 2
a = optimvar('a',2);
objfunction =@(A,a)A.*exp(a(1).*tdata).*cos(a(2).*tdata);
response = fcn2optimexpr(objfunction,A,a,"ReuseEvaluation",true) % 200-by-1, not 200-by-2
response =
200×1 Nonlinear OptimizationExpression array with properties: IndexNames: {{} {}} Variables: [1×1 struct] containing 2 OptimizationVariables See expression formulation with show.
obj = sum((response - ydata).^2);
%%%%%%%%error appear when run the optimproblem
lsqprob = optimproblem;
lsqprob.Objective=obj;
x0.A = 1/2; % was [1/2 3/2]
x0.a = [-1/2,-3/2];
show(lsqprob)
OptimizationProblem : Solve for: A, a minimize : sum((((A .* exp((a(1) .* extraParams{1}))) .* cos((a(2) .* extraParams{2}))) - extraParams{3}).^2) extraParams{1}: 0.0139 0.0357 0.0462 0.0955 0.1033 0.1071 0.1291 0.1385 0.1490 0.1619 0.1793 0.2276 0.2279 0.2345 0.2434 0.2515 0.2533 0.2894 0.2914 0.2926 0.3200 0.3336 0.3570 0.3700 0.3810 0.3897 0.3959 0.4082 0.4159 0.4257 0.4349 0.4366 0.4479 0.4571 0.4728 0.4865 0.4878 0.4969 0.5070 0.5136 0.5455 0.5505 0.5517 0.5606 0.5669 0.5898 0.6714 0.6869 0.7043 0.7197 0.7199 0.7251 0.7306 0.7533 0.7628 0.7653 0.7725 0.7796 0.7889 0.7914 0.8281 0.8308 0.8355 0.8575 0.8890 0.9190 0.9336 0.9513 1.0114 1.0132 1.0212 1.0500 1.0529 1.0550 1.0595 1.1055 1.1077 1.1413 1.1447 1.1692 1.1767 1.1993 1.2054 1.2117 1.2518 1.2653 1.2942 1.3076 1.3162 1.3280 1.3368 1.3404 1.3516 1.3528 1.4082 1.4199 1.4561 1.4604 1.4678 1.4693 1.4726 1.4951 1.5179 1.5255 1.5323 1.5397 1.5856 1.5924 1.6150 1.6406 1.6410 1.6416 1.6492 1.6496 1.7035 1.7065 1.7256 1.7391 1.7558 1.7558 1.8059 1.8481 1.8662 1.8769 1.8971 1.9389 1.9432 1.9473 1.9622 1.9653 1.9664 1.9672 2.0362 2.0391 2.0603 2.0676 2.0845 2.0972 2.1181 2.1281 2.1952 2.2294 2.2341 2.2445 2.2538 2.2612 2.2641 2.2716 2.2732 2.2966 2.3247 2.3271 2.3375 2.3407 2.3408 2.3766 2.3829 2.3845 2.3856 2.4002 2.4008 2.4429 2.4442 2.4519 2.4529 2.4636 2.4704 2.4775 2.4925 2.5222 2.5474 2.5591 2.6061 2.6079 2.6727 2.7002 2.7081 2.7174 2.7319 2.7400 2.7401 2.7472 2.7516 2.7878 2.7882 2.8020 2.8020 2.8262 2.8344 2.8507 2.8684 2.8715 2.8725 2.8779 2.8785 2.8792 2.8857 2.8947 2.9118 2.9884 extraParams{2}: 0.0139 0.0357 0.0462 0.0955 0.1033 0.1071 0.1291 0.1385 0.1490 0.1619 0.1793 0.2276 0.2279 0.2345 0.2434 0.2515 0.2533 0.2894 0.2914 0.2926 0.3200 0.3336 0.3570 0.3700 0.3810 0.3897 0.3959 0.4082 0.4159 0.4257 0.4349 0.4366 0.4479 0.4571 0.4728 0.4865 0.4878 0.4969 0.5070 0.5136 0.5455 0.5505 0.5517 0.5606 0.5669 0.5898 0.6714 0.6869 0.7043 0.7197 0.7199 0.7251 0.7306 0.7533 0.7628 0.7653 0.7725 0.7796 0.7889 0.7914 0.8281 0.8308 0.8355 0.8575 0.8890 0.9190 0.9336 0.9513 1.0114 1.0132 1.0212 1.0500 1.0529 1.0550 1.0595 1.1055 1.1077 1.1413 1.1447 1.1692 1.1767 1.1993 1.2054 1.2117 1.2518 1.2653 1.2942 1.3076 1.3162 1.3280 1.3368 1.3404 1.3516 1.3528 1.4082 1.4199 1.4561 1.4604 1.4678 1.4693 1.4726 1.4951 1.5179 1.5255 1.5323 1.5397 1.5856 1.5924 1.6150 1.6406 1.6410 1.6416 1.6492 1.6496 1.7035 1.7065 1.7256 1.7391 1.7558 1.7558 1.8059 1.8481 1.8662 1.8769 1.8971 1.9389 1.9432 1.9473 1.9622 1.9653 1.9664 1.9672 2.0362 2.0391 2.0603 2.0676 2.0845 2.0972 2.1181 2.1281 2.1952 2.2294 2.2341 2.2445 2.2538 2.2612 2.2641 2.2716 2.2732 2.2966 2.3247 2.3271 2.3375 2.3407 2.3408 2.3766 2.3829 2.3845 2.3856 2.4002 2.4008 2.4429 2.4442 2.4519 2.4529 2.4636 2.4704 2.4775 2.4925 2.5222 2.5474 2.5591 2.6061 2.6079 2.6727 2.7002 2.7081 2.7174 2.7319 2.7400 2.7401 2.7472 2.7516 2.7878 2.7882 2.8020 2.8020 2.8262 2.8344 2.8507 2.8684 2.8715 2.8725 2.8779 2.8785 2.8792 2.8857 2.8947 2.9118 2.9884 extraParams{3}: 2.0062 1.9117 1.9451 1.7782 1.6691 1.7957 1.6789 1.6345 1.6244 1.5602 1.4110 1.2761 1.2759 1.2219 1.3405 1.1280 1.1478 0.9800 0.9390 1.0025 0.8799 0.9476 0.7227 0.6102 0.7500 0.6871 0.5583 0.4173 0.4596 0.4391 0.4314 0.3866 0.3900 0.3491 0.1715 0.1390 0.1485 0.1298 0.1048 0.1034 -0.2258 -0.0634 0.0514 -0.1410 -0.0224 -0.1391 -0.4037 -0.4280 -0.5918 -0.5227 -0.4047 -0.5213 -0.5369 -0.6361 -0.6045 -0.5904 -0.5894 -0.6573 -0.6627 -0.5084 -0.8589 -0.5481 -0.6861 -0.6632 -0.8761 -0.8185 -0.8026 -0.7585 -0.9043 -0.7540 -0.8698 -0.7691 -0.7267 -0.7484 -0.6931 -0.6676 -0.7817 -0.6879 -0.7687 -0.7692 -0.6036 -0.6415 -0.6378 -0.5622 -0.5308 -0.5288 -0.4233 -0.4256 -0.4606 -0.5075 -0.4911 -0.3564 -0.5349 -0.4219 -0.4676 -0.2408 -0.1933 -0.2467 -0.2399 -0.4066 -0.1866 -0.3450 -0.3183 -0.1385 -0.2001 -0.0936 -0.0073 0.0150 -0.0618 0.0522 0.0283 0.0799 0.0693 0.0952 0.0882 0.0905 0.1923 -0.0128 0.1172 0.0636 0.1705 0.2739 0.2979 0.1754 0.2244 0.2855 0.2570 0.3000 0.3117 0.2195 0.2725 0.1359 0.3776 0.2536 0.2141 0.2806 0.1977 0.2953 0.2551 0.4452 0.3561 0.0897 0.2936 0.1607 0.2369 0.2631 0.3027 0.2279 0.3566 0.2017 0.2442 0.2666 0.2206 0.2746 0.2355 0.1383 0.0633 0.3188 0.1458 0.1869 0.2187 0.1624 0.0903 0.1162 0.1397 0.2454 0.0775 0.1884 0.1459 0.0898 0.0169 0.0639 0.0499 -0.0479 0.0320 -0.0534 -0.0065 0.0190 -0.0751 -0.0358 0.0088 -0.1475 0.0515 0.1325 0.0298 -0.0652 -0.0131 -0.1252 0.0757 0.0041 -0.0127 -0.1301 -0.0467 -0.1099 -0.0928 -0.1112 -0.1451 -0.1397 -0.0957 -0.2175
[sol ,fval] = solve(lsqprob,x0)
Solving problem using lsqnonlin. Local minimum found. Optimization completed because the size of the gradient is less than the value of the optimality tolerance.
sol = struct with fields:
A: 2.0035 a: [2×1 double]
fval = 0.9375
Alan Weiss
MATLAB mathematical toolbox documentation

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