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Moreover, we prove that the minimum of the optimization problem can be approximated by smooth functions.
The resulting problem can be approximated by a finite-dimensional linear programming problem.
The exact solution of the boundary value problem can be approximated by computationally cheaper fluxes which modify certain physical quantities.
The original problem can be approximated by a deterministic constrained MPC problem for the conditional mean by absorbing the state estimates' covariances into the constraints.
Since P ( O | λ ) = Σ a l l q P ( O, q | λ ), where q is one sequence of states from the set of all possible state sequences corresponding to the walk we want to generate, the problem can be approximated by: O * ≃ arg max O ( max q P ( O | q, λ ) P ( q | λ ) ). (8).
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The LQ problem is an important class of stochastic optimization problems, which provides a basic knowledge for more general problems since lots of nonlinear problems can be approximated by LQ problem reasonably.
It has been long known that the problem can be approximated within a factor of H k)="∑i= 1k(1/i) by the greedy heuristic, but no better bound has been shown except for the case of unweighted subsets.
Therefore, the optimization problem can be approximated as (30).
Problem (6.22) can be approximated by the difference problem (6.23).
Then problem (6.6) can be approximated by a finite-difference problem (scheme) (6.7).
In the narrow neck Ω2 the boundary value problem (2 -(4) can be approximated by the one-dimensional boundary value problem D u z z = - 1 u ( 0 ) = 0, u ( L ) = u H, where the value at the interface u(L) = u H is yet unknown.
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