In this case, we might propose a quadratic model of the form
2.
LINCOA solves linearly constrained optimization problems without using derivatives of the quadratic models by interpolation.
3.
BOBYQA solves bound constrained optimization problems without using derivatives of the quadratic models by interpolation.
4.
The same as NEWUOA, LINCOA constructs the quadratic models by the least Frobenius norm updating technique.
5.
The same as NEWUOA, BOBYQA constructs the quadratic models by the least Frobenius norm updating technique.
6.
Plugging the result back into, we now have a quadratic model of the value at time i:
7.
For instance, we could have chosen the restricted quadratic model y = \ beta _ 1 x ^ 2.
8.
In this case ( assuming that the first regressor is constant ) we have a quadratic model in the second regressor.
9.
SQP methods solve a sequence of optimization subproblems, each of which optimizes a quadratic model of the objective subject to a linearization of the constraints.
10.
A major difference between them is that UOBYQA constructs quadratic models by objective function at ( n + 1 ) ( n + 2 ) / 2 points.
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