Interior-point geometry offers a straightforward approach to constrained sampling and optimization on polyhedra, eliminating reflections and ad hoc projections. We exploit the Dikin log-barrier to define a Dikin--Langevin diffusion whose drift and noise are modulated by the inverse barrier Hessian. In continuous time, we establish a boundary no-flux property; trajectories started in the interior remain in $U$ almost surely, so feasibility is maintained by construction. For computation, we adopt a discretize-then-correct design: an Euler--Maruyama proposal with state-dependent covariance, followed by a Metropolis--Hastings correction that targets the exact constrained law and reduces to a Dikin random walk when $f$ is constant. Numerically, the unadjusted diffusion exhibits the expected first-order step size bias, while the MH-adjusted variant delivers strong convergence diagnostics on anisotropic, box-constrained Gaussians (rank-normalized split-$\hat{R}$ concentrated near $1$) and higher inter-well transition counts on a bimodal target, indicating superior cross-well mobility. Taken together, these results demonstrate that coupling calibrated stochasticity with interior-point preconditioning provides a practical, reflection-free approach to sampling and optimization over polyhedral domains, offering clear advantages near faces, corners, and in nonconvex landscapes.

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