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I'm trying to solve a nonlinear optimization problem with a matrix for linear constratins using MKL (Intel C++ 16.0).

Although aware that some bounds can be set for each variable x_i, e.g., LB_i <= x_i <= UB_i (like the __usage example__),

not quite sure how to impose additional constratins in matrix forms such that: Ax = b where A is m-by-n matrix; i.e. there are m constraints for variables x.

I'm actually trying to make a transition from using 'fminon' (http://kr.mathworks.com/help/optim/ug/fmincon.html) function of MATLAB to using MKL.

Is there any way? Thanks.

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MKL does not contain routines for nonlinear optimization (with or without constraints). It does provide a routine based on trust-regions for minimizing the sum of squares of functions, possibly with bound constraints, but this is probably not the type of problem that you wish to solve. In other words, there is no corresponding routine in MKL to Matlab's **fmincon**.

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