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{{Machine learning bar}}
The '''proper generalized decomposition''' ('''PGD''') is an [[iterative method|iterative]] [[numerical method]] for solving [[boundary value problem]]s (BVPs), that is, [[partial differential equation]]s constrained by a set of boundary conditions. The PGD algorithm computes an approximation of the solution of the BVP by successive enrichment. This means that, in each iteration, a new component (or ''mode'') is computed and added to the approximation. The more modes obtained, the closer the approximation is expected to be to its theoretical solution. By selecting only the first PGD modes, a [[reduced order model]] of the solution is obtained. Because of this, PGD is considered a [[dimensionality reduction]] algorithm.
== Description ==
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