Author_Institution :
Dept. of Math., Notre Dame Univ., IN, USA
Abstract :
Let (X, <,>) be a Hilbert space, MT = M : X→ X linear self-adjoint map, u0, u1,......um∈ X, a ∈ X, Z be a closed vector subspace in X. Consider the following optimization problem: f(x) = (((< x, Mx>)/2)+(0,x>)+((1/2)Σ(i,j=1,m)liji,x>)) → min (1) x ∈ a + Z (2). We assume that the matrix L = (lij) is symmetric and f is convex. Suppose that we can solve (numerically or analytically) the problem of the form fv(x) = ((()/2) + ()) (3) x∈ a + Z (4) for any v ∈ X. The question we address in this paper is whether it is possible to obtain the optimal solution to (1), (2) based on the information obtained by solving problems (3), (4).
Keywords :
Hilbert spaces; Newton method; control system analysis; linear quadratic control; minimax techniques; perturbation techniques; Hilbert space; Newton´s direction; closed vector subspace; descent direction; finite-rank perturbation; infinite-dimensional optimization; linear self-adjoint map; linear-quadratic control problem; minimax version; multicriteria linear-quadratic control; path following algorithm; primal-dual algorithm; quadratic constraint; Algorithm design and analysis; Ear; Equations; Hilbert space; Information analysis; Mathematics; Minimization methods; Optimal control; Symmetric matrices; USA Councils;