# A note on optimization in $\mathbb{R}^n$

## TL;DR

This paper introduces an algorithm for constrained optimization in n-dimensional real space, utilizing fundamental analysis tools and the Banach fixed point theorem to establish its main results.

## Contribution

It presents a novel algorithm for constrained optimization in imensional real space based on fixed point theory and basic analysis techniques.

## Key findings

- Algorithm effectively solves constrained optimization problems.
- Uses Banach fixed point theorem for proof of convergence.
- Provides a theoretical foundation for the algorithm's validity.

## Abstract

In this article, we develop an algorithm suitable for constrained optimization in $\mathbb{R}^n$. The results are developed through standard tools of n-dimensional real analysis and basic concepts of optimization. Indeed, the well known Banach fixed point theorem has a fundamental role in the main result establishment.