This chapter presents a recursive optimization methodology for solving the optimal power flow (OPF) problem in monopolar DC networks using a novel product-based approximation. By leveraging the multivariable Taylor series expansion, the nonlinear power balance equations in the OPF formulation are transformed into linearized expressions around a predefined operating point. This transformation simplifies the complex, non-convex problem into a convex quadratic programming model suitable for efficient numerical computation. The chapter introduces the linear approximation of the product function, highlights its application in recursive OPF modeling, and provides a detailed Julia-based implementation. Numerical validations are performed on a benchmark test system, including a six-bus MT-HVDC network, to demonstrate the effectiveness of the proposed approach. Key results show that the recursive methodology achieves high accuracy with minimal error compared to exact OPF solutions while maintaining computational efficiency. The iterative process systematically refines the solution, ensuring convergence to an optimal result. The product-based approximation framework is particularly beneficial for modern power systems with dispersed generation and renewable energy integration, as it ensures numerical stability and reduces computational complexity. This chapter concludes with a discussion on the practical implications of the proposed methodology and its potential applications in real-world scenarios, making it a valuable tool for large-scale energy systems analysis and optimization.