Central difference is a numerical method used to approximate the derivative of a function by using the average of the function's values at points on either side of a target point. This approach provides a more accurate estimate of the derivative compared to forward or backward difference methods, as it takes into account information from both directions. It’s particularly useful in finite difference methods and numerical differentiation techniques to solve problems involving derivatives.
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