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The game was solved as a standard pursuit-evasion game, using a surrogate function approach.
In this case, we are using a surrogate function approach, which gives an approximation of the solution.
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Much of the existing layout design literature that uses a surrogate function for flow distance or for simplified objectives may be entrapped into local optimum; and subsequently lead to a poor layout design due to the multiple-attribute decision making (MADM) nature of a layout design decision.
Instead, they used a surrogate function Ψ ^ (x ) which greatly overestimates the potential for significance when the margin x is close to n.
(2.5) Then (varphi ( Y_{k} ) + langle G_{k},Y - Y_{k} rangle) is used as a surrogate function of (varphi ( Y )).
Her sister suggests using a surrogate.
The goal is to estimate ρ, γ and λ under the framework of maximum likelihood estimation (MLE), using LPAC as a surrogate function for the original intractable likelihood function (1).
Two different approaches were used to solve the problem: a surrogate function approach in Sections 4 and 5; the game was solved in terms of capacity.
The problem is posed in terms of optimizing capacity, and it is solved in two ways: firstly, a surrogate function approach is used to approximate it as a pursuit-evasion game; secondly, the game is solved without that approximation.
The MM principle says we should minorize the objective function by a surrogate function and maximize the surrogate function.
The gbm function also uses a surrogate split method.
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