This analysis reduces to previous analyses of the cluster problem for unconstrained optimization under suitable assumptions.
We show that the proposed algorithm is globally and locally superlinearly convergent under suitable assumptions.
It exhibits built-in flux equilibration and is equivalent to the energy error up to data oscillation, under suitable assumptions.
Consequently, under suitable assumptions, the complexity of regression queries is at most exponential in its contextual width.
In particular this implies, under suitable assumptions, the existence of conditional densities for certain functionals of a given stochastic process.
Under suitable assumptions on the frequency, we establish upper and lower bounds for the upper transport exponents.
This hybrid algorithm is intermediate between the local algorithm and a globally convergent one for which, under suitable assumptions, convergence to KKT points can be proved.
We show that, under suitable assumptions, it is possible to reconstruct the lost data using an straightforward algorithm with the capability of associating an optimal filter order.
Under suitable assumptions, a state feedback control law with state-dependent switching is designed by using the convex combination method and the adding a power integrator technique.
Furthermore, such schemes can achieve second-order convergence at KKT points, at unconstrained points in the reduced-space, and at infeasible points under suitable assumptions when the problem exhibits a specific separable structure.
The convergence order of some widely-applicable reduced-space lower bounding schemes is also analyzed, and it is shown that such schemes can achieve first-order convergence under suitable assumptions.
