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As far as I know, most machine learning algorithms solve unconstrained optimization problems, i.e., if we were to unroll all the neurons into symbolic expressions we would end up with a massive objective function and no constraints.

I read somewhere that SVM algorithms solve constrained problems instead, so I am wondering:

Which machine learning algorithms actually solve constrained optimization problems?

Just to clarify, my question is with respect to using constrained optimization as part of the algorithm, not feeding constrained problems in.

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    $\begingroup$ w.r.t. to your last paragraph you may wish to rephrase the title; my natural answer to your current title question is: none of them. $\endgroup$ Oct 24, 2019 at 13:22
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    $\begingroup$ Good point, updated the title. $\endgroup$ Oct 24, 2019 at 16:22

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As the borders between statistics and machine learning are diffuse, I take freedom to include statistics. Some links:

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