Error using Nonlcon: "Not Enough Input Arguments"

Attempting to apply non linear constraints to my ga optimization, however, I keep recieving this error:

I can't seem remedy this issue. The design vector should be a 1x40 vector x and is what is being fed to nonlcon. Hope someone more experienced can shed some light on my problem. I attached my main script and nonlcon function below. Thanks.

Main Script:

%%   Male Macro/Caloric Requirements & Optimization
clear all
clc

% Physical attributes that determine macros/calorie requirements for avg.
% Male: Age=25/Height=5'9"/Weight=197.9 lb/Goal=Lose 0.5 lb per week/ % Activity Level=Lightly Active 3 times per week

% Macro/Caloric requirements
% Protein: 142g
% Carbs: 310g
% Fats: 66g
% Calories: 2,325 calories


% fitnessfcn is the cost function that calculates the grocery cost.

fitnessfcn = @(x) 0.23*x(1)+0.16*x(2)+1.1*x(3)+0.39*x(4)+1.45*x(5)+0.22*x(6)+0.54*x(7)+0.84*x(8)+0.52*x(9)+3.24*x(10)+...
    0.14*x(11)+0.53*x(12)+0.23*x(13)+0.29*x(14)+0.35*x(15)+1*x(16)+0.84*x(17)+0.26*x(18)+0.62*x(19)+0.17*x(20)+...
    1.9*x(21)+0.85*x(22)+0.15*x(23)+0.32*x(24)+0.32*x(25)+...
    0.4*x(26)+0.48*x(27)+0.15*x(28)+1.31*x(29)+1.19*x(30)+0.9*x(31)+0.14*x(32)+0.16*x(33)+0.3*x(34)+0.28*x(35)+...
    0.79*x(36)+0.25*(37)+0.89*x(38)+1.38*x(39)+0.86*x(40);

% Number of design variables.
nvars = 40;

% Design variable definitions
% Dairy: x1=whole milk/x2=butter/x3=greek yogurt/x4=shredded cheddar cheese/x5=casein protein 
% x26=cottage cheese/x27=ice cream/x28=sour cream 
% Protein: x6=eggs/x7=bacon/x8=sausage links/x9=turkey sausage/x10=ribeye steak 
% x29=chicken breasts/x30=ground beef/x31=ground turkey 
% Grains: x11=oatmeal/x12=bagel/x13=whole wheat bread/x14=flour tortillas/x15=honey nut cheerios 
% x32=brown rice/x33=white rice/x34=quinoa 
% Vegetables: x16=white onion/x17=bell peppers/x18=portobello mushrooms/x19=spinach/x20=potatoe 
% x35=broccolli/x36=lettuce/x37=cauliflower
% Fruit: x21=blueberries/x22=strawberries/x23=bananas/x24=mango/x25=avocado
% x38=granny smith apple/x39=tomatoe/x40=orange

% A*x <= b
% A=linear inequality coefficents
% b=linear ineqaulity constraints (right hand side values)

A=[-8,0,-16,-6,-24,-6,-4,-9,-9,-21,-5,-9,-4,-3,-3,-2,-1,-5,-2,-3,-1,-1,-1,0,-1,-14,-4.5,0,-19,-20,-19,-4,-4,-6,-1,-1,-2,0,-1,-1;... % Protein macro constraint
    -12,0,-6,0,-3,0,0,-4,-5,0,-27,-53,-21,-16,-30,-15,-7,-6,-3,-26,-20,-11,-27,-27,-4,-3,-22.5,0,0,0,0,-34,-37,-28,-4,-3,-5,-29,-6,-22;... % Carb macro constraint
    -8,-11,0,-9,-1,-5,-7,-14,-5,-23,-3,-1,-2,-2,-2,0,0,-1,0,0,0,0,0,0,-7,-1,-13.5,-6,-10,-23,-17,-1,0,-2,0,0,0,0,0,0;... % Fat macro constraint
    -1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0;... % Dairy variety constraint
    0,0,0,0,0,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,-1,0,0,0,0,0,0,0,0,0;... % Protein variety constraint
    0,0,0,0,0,0,0,0,0,0,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,-1,0,0,0,0,0,0;... % Grains variety constraint
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,-1,0,0,0;... % Vegetables variety constraint
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,-1,-1,-1,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,-1;... % Fruit variety constraint
    149,100,90,110,120,70,80,180,120,280,150,270,110,90,140,64,19,26,25,110,80,48,105,110,78,79,2.25,60,160,280,230,160,160,170,25,10,20,110,27,87;... %calorie upper limit
    -149,-100,-90,-110,-120,-70,-80,-180,-120,-280,-150,-270,-110,-90,-140,-64,-19,-26,-25,-110,-80,-48,-105,-110,-78,-79,-2.25,-60,-160,-280,-230,-160,-160,-170,-25,-10,-20,-110,-27,-87]; %calorie lower limit 
b=[-142;... % Protein macro constraint in grams
   -310;... % Carbs macro constraint in grams
   -66;... % Fats macro constraint in grams
   -3;... % Dairy variety for three meals
   -3;... % Protein variety for three meals
   -3;... % Grains variety for three meals
   -3;... % Vegetables variety for three meals
   -3;... % Fruit variety for three meals
   2475;... % Upper limit of calorie intake
   -2175]; % Lower limit of calorie intake

% Aeq * x = beq
% Aeq=linear eqaulity coefficients
% beq=linear eqaulity constraints (right hand side values) 
Aeq=[]; 
beq=[];

% Lower and upper bounds for all fourty design variables 
Lb=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0];
Ub=[2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2];

intcon=1:40; % restricts all fourty design variables to only being able to be integers 
[x,fval,exitflag,output,population] = ga(fitnessfcn,nvars,A,b,Aeq,beq,Lb,Ub,nonlcon,intcon);
% x: Best point that ga located during its iterations % fval: Fitness (objective) function evaluated at x 
% exitflag: Integer giving the reason ga stopped iterating
%   Positive Numbers = reached a convergence stop condition
%   0 = maximum number of generations exceceeded
%   -2 = No feasible point found
%   Other negative numbers = Prematurely terminated
% output: Structure containing output from each generation and other information % about algorithm performance % population: Matrix whose rows contain the members of the final population % plot fitness value vs. generations
opts = optimoptions(@ga,'PlotFcn',{@gaplotbestfun});
[X,Fval] = ga(fitnessfcn,nvars,A,b,Aeq,beq,Lb,Ub,nonlcon,intcon,opts);

% Calculate values of macronutrients (protein, carbs, fats), cost/solution, % and calories/solution from x (solution vector).
protein_macro_values_vector=[8,0,16,6,24,6,4,9,9,21,5,9,4,3,3,2,1,5,2,3,1,1,1,0,1,14,4.5,0,19,20,19,4,4,6,1,1,2,0,1,1]';
carb_macro_values_vector=[12,0,6,0,3,0,0,4,5,0,27,53,21,16,30,15,7,6,3,26,20,11,27,27,4,3,22.5,0,0,0,0,34,37,28,4,3,5,29,6,22]';
fat_macro_values_vector=[8,11,0,9,1,5,7,14,5,23,3,1,2,2,2,0,0,1,0,0,0,0,0,0,7,1,13.5,6,10,23,17,1,0,2,0,0,0,0,0,0]';
grocery_cost_values_vector=[0.23,0.16,1.1,0.39,1.45,0.22,0.54,0.84,0.52,3.24,0.14,0.53,0.23,0.29,0.35,1,0.84,0.26,0.62,0.17,1.9,0.85,0.15,0.32,0.32,0.4,0.48,0.15,1.31,1.19,0.9,0.14,0.16,0.3,0.28,0.79,0.25,0.89,1.38,0.86]';
calorie_values_vector=[149,100,90,110,120,70,80,180,120,280,150,270,110,90,140,64,19,26,25,110,80,48,105,110,78,79,2.25,60,160,280,230,160,160,170,25,10,20,110,27,87]';

% Solution vector -> [protein in g, carbs in g, fats in g, cost, calories].
solution_vector(1)=x*protein_macro_values_vector;
solution_vector(2)=x*carb_macro_values_vector;
solution_vector(3)=x*fat_macro_values_vector;
solution_vector(4)=x*grocery_cost_values_vector;
solution_vector(5)=x*calorie_values_vector;

nonlcon:

% Non-linear constraints for Non-Linear Constraint Example
function [ c,ceq ] = nonlcon(x)
c(1) = -(x(1)/x(1))-(x(2)/x(2))-(x(3)/x(3))-(x(4)/x(4))-(x(5)/x(5))-(x(26)/x(26))-(x(27)/x(27))-(x(28)/x(28))+3; % Diversity variety constraint dairy
c(2) = -(x(6)/x(6))-(x(7)/x(7))-(x(8)/x(8))-(x(9)/x(9))-(x(10)/x(10))-(x(29)/x(29))-(x(30)/x(30))-(x(31)/x(31))+3; % Diversity variety constraint protein
c(3) = -(x(11)/x(11))-(x(12)/x(12))-(x(13)/x(13))-(x(14)/x(14))-(x(15)/x(15))-(x(32)/x(32))-(x(33)/x(33))-(x(34)/x(34))+3; % Diversity variety constraint grains
c(4) = -(x(16)/x(16))-(x(17)/x(17))-(x(18)/x(18))-(x(19)/x(19))-(x(20)/x(20))-(x(35)/x(35))-(x(36)/x(36))-(x(37)/x(37))+3; % Diversity variety constraint vegetables
c(5) = -(x(21)/x(21))-(x(22)/x(22))-(x(23)/x(23))-(x(24)/x(24))-(x(25)/x(25))-(x(38)/x(38))-(x(39)/x(39))-(x(40)/x(40))+3; % Diversity variety constraint fruits
ceq=[];
    
end

Answer 1

[Torsten]

[x,fval,exitflag,output,population] = ga(fitnessfcn,nvars,A,b,Aeq,beq,Lb,Ub,@nonlcon,intcon);

instead of

[x,fval,exitflag,output,population] = ga(fitnessfcn,nvars,A,b,Aeq,beq,Lb,Ub,nonlcon,intcon);

and

[X,Fval] = ga(fitnessfcn,nvars,A,b,Aeq,beq,Lb,Ub,@nonlcon,intcon,opts);

instead of

[X,Fval] = ga(fitnessfcn,nvars,A,b,Aeq,beq,Lb,Ub,nonlcon,intcon,opts);

Although I don't understand your constraints. x(i)/x(i) will always be 1 ...

[Brendan Wilhelm]

I appreciate the help. I constrained it in that way to ensure that at minimum 3 different types of protein,grains,etc. would be selected for a 'meal plan'

[Torsten]

Test whether "intlinprog" instead of "ga" works for your problem. It will be way faster.