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Consequently, there is much interest in approximation and heuristic algorithms that can find near optimal solutions within reasonable running time.
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All those problems are NP-hard, and thus we are interested in approximation algorithms.
In particular, Eric is interested in approximations that retain their accuracy even in systems where mean-field-based methods such as density functional theory and even coupled cluster theory are grossly incorrect due to the breakdown of the statistically independent particle approximation.
Here we are interested in approximations of signals in ({L}_{1} -space.
Since Selman and Kautz's seminal work on the use of Horn approximation to speed up the querying of knowledge bases, there has been great interest in Boolean approximation for AI applications.
Therefore, great interest in numerical approximation techniques in non-Newtonian fluid mechanics is observed in recent years; see, for instance, [14, 15, 19, 38 40], among many others.
All our problems are NP-hard; so we are interested in their approximation algorithms.
We are interested in the approximation of a steady hyperbolic problem.
Assuming some knowledge of the variational formulation of PDEs and of finite element/boundary element methods, the book is suitable for PhD students and researchers interested in numerical approximation of partial differential equations and scientific computing.
In this section, we are interested in the approximation solutions of (EP) associated with both the constraint set and the objective function of (EP).
As mentioned earlier, we are interested in the approximation (overline{varphi}_{1}) to (bar{varphi}(t_{1})), which is used to construct a new scheme.
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