Interior-Point Methods

Interior-Point Methods are algorithms used to solve complex optimization problems by exploring the interior of the feasible region, rather than its edges. Imagine navigating a maze from the inside, continuously adjusting your path to find the optimal point—these methods do this efficiently for large or constrained problems. They work by transforming the original problem with a barrier function that discourages approaching boundaries, guiding the solution toward the best possible answer. Widely used in finance, engineering, and logistics, interior-point methods are powerful tools for finding optimal solutions in high-dimensional spaces.