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The small approximation is involved in getting the solution to the approximate problem".
Other important algorithmic approaches include sequential quadratic programming, in which an approximate problem with a quadratic objective and linear constraints is solved to obtain each search step; and penalty methods, including the "method of multipliers," in which points that do not satisfy the constraints incur penalty terms in the objective to discourage algorithms from visiting them.
Our approach is to consider an approximate problem first.
We search for solutions of the approximate problem (2.7).
We next build the approximate problem for ( P ).
Because of the highly complex nature of this approximate problem, a parallel algorithm based on the filled function method is constructed to solve this approximate problem.
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We show that straightforward discrete approximations to these problems yield approximate problems which are ill-posed.
To prove Theorem 2.1, we shall consider suitable approximate problems.
But when solving the approximate problems, Spelke says, "there was zero slowdown.
Numerical examples show that MQDQ method can efficiently approximate problems in complexly shaped domains.
In order to verify solutions numerically, it is necessary to calculate the explicit a priori error estimates for approximate problems.
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