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In this case, let us denote by K ⊆ N the set indexing the functions having local minima and C k, with k ∈ K, the set of all delays being local minima of function J k ( H ˆ ), excluding the global optimum at the actual matrix delay.
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Various direct and inverse scattering problems require finding global minima of functions of several variables.
The minimum of function f is achieved in the system with average W[14].
Secondly, to find the maximum and minimum of function f(P t)) among all slots, it is necessary to consider different values of N t) along the time.
Secondly, it will be shown that the proposed Algorithm 1 is able to asymptotically find the global minimum of function J ( H ˆ, t ).
However, to ensure that the critical point ϕ(n+1) is the minimum of function r, the Hessian matrix H should be positive semi-definite [43].
According to the equality f(P t))=β, we can find the maximum and minimum of function f(P t)) among all slots instead of finding the maximum and minimum of β directly.
The estimated maximum of function d f, d max equals to the estimated maximum of the function value at the domain boundary, and the estimated minimum of function d f, d min equals to the estimated minimum of the function value at the domain boundary.
Since f(P t)) is a monotonically increasing function of P t), the maximum and the minimum of function f(P t)) can be achieved when P t)=Pmax and P t)=Pmin, respectively.
Our numerical investigation shows that for the initialization vector (boldsymbol {phi }^{(0)}triangleq [0,0,0,0]), the Hessian matrix H is always positive and hence the critical point ϕ(n+1) is indeed the minimum of function r.
A genetic algorithm aiming for finding the global minimum and multiple deep local minima of a function exhibiting a complex landscape is studied.
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CEO of Professional Science Editing for Scientists @ prosciediting.com