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The method is based on a recently developed sequential approximation (SA) method, which approximates a target function using one data point at each step and avoids matrix operations.
The framework uses non-convex and convex sequential approximation optimization.
This paper presents an improved sequential approximation optimization (SAO) algorithm that is suitable for structural design optimization tasks.
It is a sequential approximation method, in which a multi-objective optimization with approximation models subject to design variable move limits is iterated until convergence.
The quadratic model of RSM associated with the sequential approximation optimization (SAO) method was used to find optimum values of machining parameters.
This paper performed the optimization of grid fins shape of a launch vehicle based on Sequential Approximation Optimization (SAO) and Computational Fluid Dynamics CFDD) simulation coupling.
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Dual methods based on sequential approximations are usually employed for solving topology optimization problems.
From the condition of the theorem, we have u ( 0 ) ( t ) and v ( 0 ) ( t ) ∈ B. We will prove that the other sequential approximations satisfy this condition.
The basic principle of sequential approximations is to replace the given problem by a sequence of quadratic sub-problems that are easier to solve [31, 32].
The sequential approximations can efficiently remove background variation in the double-mutation screens and give increasingly accurate estimates of the single-mutant fitness measurements.
The optimization technique used relies on sequential approximate optimization through multipoint approximations.
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