A computational graph is a graphical representation of a mathematical expression or a sequence of operations. It is typically used in deep learning frameworks to represent and compute complex mathematical models.

  When it comes to handling non-linear operations, a computational graph is designed to handle them through a combination of elementary operations, such as addition, multiplication, and composition of functions.

  Non-linear operations, such as activation functions like sigmoid, tanh, or ReLU, are commonly used in neural networks to introduce non-linearities and capture complex relationships in the data. These activation functions are typically applied element-wise on the output of a linear transformation (e.g., matrix multiplication) in each layer of the network.

  In a computational graph, these non-linear operations are represented by nodes in the graph, where the inputs to the node are the outputs of the preceding nodes. Each node performs the non-linear operation on its inputs and produces an output. The output then becomes the input to subsequent nodes in the graph, forming a sequential flow of transformations.

  The computational graph allows for efficient evaluation of all nodes in the graph by propagating the inputs through the graph and computing the final output. This is typically done through a process called forward propagation. The graph can also be used for backpropagation, which is used to compute gradients with respect to the input parameters for training the model.

  In summary, a computational graph handles non-linear operations by representing them as nodes in the graph and propagating inputs through the graph to compute the final output. This allows for efficient evaluation of complex mathematical models, including those involving non-linearities.