The transposed Jacobian matrix of the gradient is the matrix obtained by taking the transpose of the Jacobian matrix of the gradient vector field. This matrix represents the linear transformation that maps changes in the input space to changes in the output space of the gradient vector field. It is useful in various mathematical and computational applications, such as optimization algorithms and solving systems of differential equations. **

The multidimensional differential quotient with the Jacobian matrix is a way to represent the derivative of a vector-valued function. It is a generalization of the derivative for functions of several variables. The Jacobian matrix contains all the first-order partial derivatives of the vector-valued function with respect to its input variables. The multidimensional differential quotient with the Jacobian matrix allows us to understand how small changes in the input variables affect the output of the function, making it a powerful tool in fields such as physics, engineering, and economics. **

The gradient is a vector of partial derivatives of a scalar function with respect to its variables, while the Jacobian matrix is a matrix of partial derivatives of a vector-valued function with respect to its variables. In other words, the gradient represents the rate of change of a scalar function in different directions, while the Jacobian matrix represents the rate of change of a vector-valued function in different directions. Additionally, the gradient is a special case of the Jacobian matrix when the vector-valued function is a scalar function. **

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The Jacobian matrix being surjective means that it has full rank, which implies that it is onto. If the Jacobian matrix has exactly 3 preimages for each point, it means that the function is locally 3-to-1. However, the surjectivity of the Jacobian matrix does not necessarily guarantee that there are exactly 3 preimages for each point. It is possible for the Jacobian matrix to be surjective and have a different number of preimages for each point. **

The Jacobian matrix being surjective means that its rank is equal to the dimension of the target space. If the Jacobian matrix has exactly 3 preimages for every point, it implies that the function is locally 3-to-1. Whether the Jacobian matrix is surjective and has exactly 3 preimages for every point depends on the specific function being considered. It is possible for a function to have a surjective Jacobian matrix and 3 preimages for every point, but it is not a general rule. **

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