62 lines
2.6 KiB
Mathematica
62 lines
2.6 KiB
Mathematica
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function dX = apply_recursive_differentiation(model,x,requested);
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dX = [];
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% Compute all evaluation-based derivatives df(x)
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for i = 1:length(model.evaluation_scheme)
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if isequal(model.evaluation_scheme{i}.group,'eval')
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for j = model.evaluation_scheme{i}.variables
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k = model.evalMap{j}.variableIndex;
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if any(requested(model.evalMap{j}.computes))
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z{i,j} = model.evalMap{j}.properties.derivative(x(k));
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end
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end
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end
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end
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% Apply chain-rule. This code is horrible
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for variable = 1:length(model.linearindicies)
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dx = zeros(length(model.c),1);
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dx(model.linearindicies(variable)) = 1;
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for i = 1:length(model.evaluation_scheme)
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switch model.evaluation_scheme{i}.group
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case 'eval'
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for j = model.evaluation_scheme{i}.variables
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k = model.evalMap{j}.variableIndex;
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r = find(dx(k));
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if ~isempty(r)%any(dx(k))
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if any(requested(model.evalMap{j}.computes))
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if length(model.evalMap{j}.computes) == 1
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%dx(model.evalMap{j}.computes) = dx(k)'*model.evalMap{j}.properties.derivative(x(k));
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dx(model.evalMap{j}.computes) = dx(k)'*z{i,j};
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else
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%dx(model.evalMap{j}.computes) = dx(k).*model.evalMap{j}.properties.derivative(x(k));
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dx(model.evalMap{j}.computes) = dx(k).*z{i,j};
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end
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end
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end
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end
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case 'monom'
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computed = model.monomials(model.evaluation_scheme{i}.variables);
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for j = computed
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if requested(j)
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dp = 0;
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monomsj = model.monomtable(j,:);
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for k = find(dx' & monomsj)
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monoms = monomsj;
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monoms(k) = 0;
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r = model.monomtable(j,k);
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s = find(monoms);
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dp = dp + r*x(k)^(r-1)*dx(k)*prod((x(s)').^monoms(s));
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end
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dx(j) = real(dp);
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end
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end
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otherwise
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end
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end
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dX = [dX dx];
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end
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