Sorry, I am afraid that the available options don't work efficiently for your case. The HessPattern option is available only for the 'trust-region-reflective' algorithm, but for that algorithm you need to supply a derivative.
I am not sure what to suggest that you probably have not yet tried. For the default 'interior-point' algorithm you can try using the HessianApproximation option set to 'lbfgs' or {'lbfgs',Positive Integer}, but that does not directly use the sparsity pattern that you know. Or, and this seems crazy, you could code a finite difference gradient in your objective funtion, bypassing MATLAB's internal one, and then you could use the 'trust-region-reflective' algorithm with the HessPattern option. I am not sure that the 'trust-region-reflective' algorithm would satisfy you anyway, as it accepts only bound constraints or only linear equality constraints.
Sorry.
Alan Weiss
MATLAB mathematical toolbox documentation
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