The interior point method is an optimization algorithm used to solve linear and nonlinear programming problems by iterating through the feasible region from within, rather than on the boundary. This approach allows for finding optimal solutions more efficiently, particularly in large-scale problems, and has gained popularity as a powerful alternative to the simplex method. It employs a barrier function to maintain the iterations within the feasible region, helping to avoid the complications that can arise when approaching the boundary of feasible solutions.
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