Just like a polytope can be written as the finite disjoint union of relative interiors of its faces, we can write the feasible set of (well-behaved...) nonlinear optimization problems as the finite disjoint union of differentiable manifolds of different dimensions. Their closures are manifolds with corners (#30080...), which together form a CW complex.
In the special case of the simplex method for LP in standard equation form:
- a basic solution is a submanifold of dimension 0 embedding into the affine space defined by the equations
- the nonbasic variables form an adapted chart of that space.
In the more general case of convex quadratic programming:
- an active set determines a submanifold (an affine subspace) of some dimension
CC: @yuan-zhou
Component: manifolds
Issue created by migration from https://trac.sagemath.org/ticket/31376