Correct the publication date of reference UY

Numerical-optimization.md

@@ -87,7 +87,7 @@ Expressing $f^{(1)}_p(\_\!\_)$ as $(\tilde{F}^{(1)}_p, \_\!\_)$ and following th
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 The operator $\big[ \tilde{F}^{(2)}_p + \lambda \big]^{-1}$ stretches $V$ along the eigenspaces of $\tilde{F}^{(2)}_p$, which are the principal curvature directions of $f$ at $p$ with respect to the inner product $(\_\!\_, \_\!\_)$. Projectively, this stretching pulls lines in $V$ away from the eigenspaces with higher eigenvalues, where $f$ curves more strongly upward or less strongly downward, and towards the eigenspaces with lower eigenvalues, where $f$ curves less strongly upward or more strongly downward. Thus, heuristically, applying $\big[ \tilde{F}^{(2)}_p + \lambda \big]^{-1}$ to the gradient descent direction $-\tilde{F}^{(1)}$ trades some of our downward velocity for downward acceleration. When we set $\lambda$ to zero, which is allowed when $\tilde{F}^{(2)}_p$ is already positive-definite, this trade is perfectly calibrated to point us toward the minimum of the second-order approximation, and the regularized Newton step reduces to the classic Newton step. When we let $\lambda$ grow, the projective action of $\big[ \tilde{F}^{(2)}_p + \lambda \big]^{-1}$ approaches the identity, and the regularized Newton step approaches $\lambda^{-1}$ times the gradient descent step.
 
-- **[UY]** Kenji Ueda and Nobuo Yamashita. [“Convergence Properties of the Regularized Newton Method for the Unconstrained Nonconvex Optimization.”](https://doi.org/10.1007/s00245-009-9094-9) *Applied Mathematics and Optimization* 62, 2010.
+- **[UY]** Kenji Ueda and Nobuo Yamashita. [“Convergence Properties of the Regularized Newton Method for the Unconstrained Nonconvex Optimization.”](https://doi.org/10.1007/s00245-009-9094-9) *Applied Mathematics and Optimization* 62, 2009.
 
 #### Positive-definite truncation