 Bounded barrier multipliers under Slater's condition

Let $c_i$ be concave and continuously differentiable, and let a strict feasible point $z$ satisfy $\delta ={\min }_i{c}_i(z)>0$. Suppose $x_k\to x_{\ast }$, $c_i(x_k)>0$, ${\mu }_k\to 0$, and the logarithmic barrier residuals $r_k=\nabla f(x_k)-{\sum }_i{\lambda }_{k,i}\nabla c_i(x_k)$ tend to zero, where ${\lambda }_{k,i}={\mu }_k/c_i(x_k)$. By concavity,The right-hand side is bounded when $\nabla f$ is continuous near $x_{\ast }$. Thus the nonnegative Lagrange multiplier estimates are bounded. A convergent subsequence gives ${\lambda }_{\ast }\ge 0$; passing to the limit in stationarity and ${\lambda }_{k,i}{c}_i(x_k)={\mu }_k$ proves the Karush-Kuhn-Tucker conditions at $x_{\ast }$. An inactive optimization constraint has $c_i(x_{\ast })>0$ and hence ${\lambda }_{k,i}\to 0$ even without selecting a subsequence. With a convex objective function, these conditions certify a global minimum. This result is conditional on the stated residual and primal convergence assumptions; it does not assert that every barrier subproblem has a minimizer.