The simplex algorithm solves linear programming optimization problems by systematically navigating the extreme points (vertices) of a convex polyhedral feasible region defined by linear inequality constraints. Moving along adjacent edges that provide the steepest gradient improvement in the objective function c^T x guarantees convergence to the global optimum.