Complex mathematical functions can be decomposed and represented in a computational graph by breaking down the function into smaller, simpler mathematical operations and representing them as nodes in the graph.

  In a computational graph, each node represents an operation or computation, such as addition, multiplication, or differentiation. The inputs to these operations are represented as edges connecting the nodes. The output of each operation is then passed on as input to subsequent operations.

  To decompose a complex mathematical function, you can break it down into a sequence of simpler operations. For example, consider the function f(x) = (x^2 + 3x) / sin(x). This function can be decomposed into three operations: squaring x, multiplying the result by 3x, and dividing the entire expression by sin(x). Each of these operations can be represented as a node in the computational graph.

  In the computational graph, the input variable x would be connected to the corresponding nodes that represent the operations. The intermediate results of each operation can be stored and used as input for subsequent operations.

  Once the computational graph is constructed, it can be used for various purposes, such as evaluating the function at a particular input value, computing derivatives using backpropagation, or optimizing the function through gradient descent.

  By decomposing complex mathematical functions into simpler operations and representing them in a computational graph, it becomes easier to visualize and understand the computations involved. It also allows for efficient computation and manipulation of the function using graph-based algorithms and techniques.