#StackBounty: #machine-learning #optimization #gradient-descent #constraint Coordinate descent with constraints

Bounty: 50

When performing constrained optimization on a smooth, convex function using coordinate descent, for what types of constraints will the algorithm work ? (i.e. converge or reach an approximate optimum within a tolerance of the constraint)

My understanding is that coordinate descent will work for

• Box constraints: e.g. \$x_1 leq -1\$ and \$x_2 leq -1\$
• Linear constraints: e.g. \$x_2 leq -x_1 -1\$
• Any others ?

In other words, how can we know whether or not coordinate descent can be applied to a contrained optimization problem ?

PS: irrespective of whether this is the right algorithm to use

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