Iterativos

Iterativos is the plural form in Spanish and Portuguese of the adjective iterative, used to describe processes or methods that proceed through repeated cycles or iterations to refine outcomes. The term denotes progress through feedback and refinement, often aiming for improved accuracy, efficiency, or adaptation to new information.

In mathematics and numerical analysis, iterative methods generate successive approximations to a solution. Examples include fixed-point iteration, the Jacobi and Gauss-Seidel methods for linear systems, and Newton-Raphson for nonlinear equations. Convergence criteria and stopping conditions are essential, and performance depends on problem structure, initial guesses, and conditioning.

In software development and project management, iterative approaches break work into cycles or sprints that deliver incremental functionality. Stakeholder feedback at the end of each iteration informs the next steps, contrasting with linear models and supporting adaptability, risk management, and continuous improvement.

In design and product development, iterative design or design thinking employs rapid prototyping, user testing, and refinement of ideas through multiple cycles to converge on a usable solution.

In data science and machine learning, iterative optimization algorithms, such as gradient descent, repeatedly adjust parameters to minimize a loss function, often using convergence criteria and hyperparameters like learning rate.

Overall, iterative methods emphasize learning from each cycle, with benefits including flexibility and early value delivery, and potential drawbacks such as longer development time if iterations are not well scoped or monitored.