| 1. | Both the objective function and the constraints might be nonlinear and nonconvex.
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| 2. | The key is in the way that the objective function is modified.
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| 3. | This information is then utilized in the objective function of the subproblem.
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| 4. | The local isometry constraint prevents the objective function from going to infinity.
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| 5. | This is the number of evaluations required to minimize the objective function.
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| 6. | The objective functions will depend on the perspective of the model's user.
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| 7. | K-means clustering also attempts to minimize the objective function shown above.
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| 8. | The standard objective function, \ phi, is usually of the form:
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| 9. | In the first part, some objective functions for single-objective optimization cases are presented.
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| 10. | The optimization of the objective function is usually performed using pairwise Jacobi rotations.
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