Support vector machines (SVMs) are used in optimization to find the best possible separating hyperplane between two classes in a dataset. The goal of SVM is to maximize the margin between the two classes by finding the hyperplane that has the greatest distance to the nearest points in either class. This optimization problem can be solved using different approaches such as quadratic programming, convex optimization, and constrained optimization. SVMs are also used in other optimization problems such as regression and anomaly detection. By using SVMs, we can efficiently solve complex optimization problems and find the best possible solution.
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