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Theorem 2.3 means that an approximately optimal solution to [SP] is an approximately optimal solution to [P] if the solution to [SP] is ϵ-feasible.
(2004) to provide the nonlinear model into a linear one which obtains an approximately optimal solution.
Theorem 2.1 and Theorem 2.2 mean that an approximately optimal solution to [SP] is also an approximately optimal solution to ([LOP^{prime}]_{k}) when the error ϵ is sufficiently small.
From Table 1, we see that Algorithm A can generate approximately optimal solution for all the test problems.
After these changes, an approximately optimal solution of model (7) is obtained by solving a linear programming model related to it.
A complex treatment was applied in Jahanshahloo et al. (2004a) to convert the nonlinear model into a linear one which provides an approximately optimal solution.
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Excellent approximately optimal solutions are also provided in closed-forms.
We propose an abstraction technique for MDPs that allows approximately optimal solutions to be computed quickly.
Noting that the key computational bottleneck in the solution of factored MDP-IPs is the need to repeatedly solve nonlinear constrained optimization problems, we show how to target approximation techniques to drastically reduce the computational overhead of the nonlinear solver while producing bounded, approximately optimal solutions.
Finally, in Table 3, we report the approximately optimal solutions and values for problem L-Mifflin with different starting points.
Therefore, simulated annealing was used to search for approximately optimal solutions using the Metropolis algorithm.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com