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The problem can be solved efficiently using the concept of the conventional waterfilling.
The approximated problem can be solved efficiently using common optimization routines.
This problem is a second order cone programming (SOCP) and can therefore be solved efficiently using standard software packages.
However, the optimization problem could be solved efficiently using a variety of constrained optimization algorithms, e.g., the interior point method.
This image separation problem can be solved efficiently using an iterative shrinkage algorithm proposed in [17] (Figure 5).
The design conditions are given in linear matrix inequalities (LMIs) terms that can be solved efficiently using existing numerical tools.
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The optimization is solved efficiently using 4D filtering in pixel-disparity space.
Nevertheless, this problem is solved efficiently using Ipopt, demonstrating the effectiveness of interior-point methods on complex, large-scale nonlinear programming problems.
The resulting integral formulas are well conditioned for single molecule cases as well as for systems with more than one macromolecule, and are solved efficiently using Krylov subspace based iterative methods such as generalized minimal residual (GMRES) or biconjugate gradient stabilized (BiCGStab) methods.
The minimization problem is modeled as a shortest path problem in an acyclic graph with nonnegative weights and is solved efficiently using Dijkstra's method.
Several LMI problems can be solved efficiently by using recently developed convex optimization algorithms [27].
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