The weight linear equality constraints, obtained through observations, are also implemented in the formulation to test the method.
However, in contrast to standard AAMs, we augment the fitting process by imposing constraints obtained from sources such as subset methods [16, 30].
The distance constraints obtained from the NMR data have been utilized to generate a family of structures, which have been refined using restrained energy minimization and dynamics.
The observer design is derived from the solution of linear matrix inequalities (LMIs), based on the solutions of algebraic constraints obtained from the unbiasedness conditions of estimation error.
We see that the cosmological constraints obtained only with GRBs are larger than those obtained with SNe Ia for a Friedmann-Robertson-Walker cosmology with mass density ΩM and vacuum energy density ΩΛ.
With this approach, depending on the instantaneous available DoF of a link, the effective capacity of each link is updated per frame, and the number of constraints obtained by the decomposition reduces from d to 1.
While our augmented AAM approach is holistic in the sense that all landmarks are modeled in one consistent framework, it uses constraints obtained from methods which only perform well on very specific landmark subsets (cf. Section 3).
Here, and whenever R ≥ 2(N − 1), a two-step solution can be found from the (N − 1) known τj values and an additional (R − 2(N − 1)) constraints obtained from rate ratios or other sources.
The first approach builds a multi-objective mixed-integer linear programming (MO-MILP) surrogate model of the optimization problem relying on piece-wise linear approximations of objectives and constraints obtained through brute-force sensitivity computation.
The design approach of the UO is derived from the solution of the linear matrix inequality (LMI), based on the new dynamic observer formulation and the solution of the algebraic constraints obtained from the unbiased estimation error.
