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Differentiating the Multipoint Expected Improvement for Optimal Batch Design

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Machine Learning, Optimization, and Big Data (MOD 2015)


This work deals with parallel optimization of expensive objective functions which are modelled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of \(q > 0\) arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis’ formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batch-sequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization.

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Part of this work has been conducted within the frame of the ReDice Consortium, gathering industrial (CEA, EDF, IFPEN, IRSN, Renault) and academic (École des Mines de Saint-Étienne, INRIA, and the University of Bern) partners around advanced methods for Computer Experiments.

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Correspondence to Sébastien Marmin .

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6 Appendix: Differential Calculus

6 Appendix: Differential Calculus

  • \(g_1\) and \(g_2\) are functions giving respectively the mean of \(\varvec{Y}(\varvec{X})\) and its covariance. Each component of these functions is either a linear or a quadratic combination of the trend function \(\varvec{\mu }\) or the covariance function C evaluated at different points of \(\varvec{X}\). The results are obtained by matrix differentiation. See the Appendix B of [21] for a similar calculus.

  • \(g_3\) (resp. \(g_4\)) is the affine (resp. linear) tranformation of the mean vector \(\varvec{m}\) into \(\varvec{m}^{(k)}\) (resp. the covariance matrix \(\varSigma \) into \(\varSigma ^{(k)}\)). The differentials are then expressed in terms of the same linear transformation:

    $$\begin{aligned} d_{\varvec{m}}\left[ g_3\right] (\varvec{h})=L^{(k)}\varvec{h}\nonumber ~~\text { and }~~d_{\varSigma }\left[ g_4\right] (H)=L^{(k)}HL^{(k)\top }. \end{aligned}$$
  • \(g_5\) is defined by \(g_5\left( \varvec{m}^{(k)},\varSigma ^{(k)}\right) =\varphi _{\varSigma _{ii}^{(k)}}\left( m_i^{(k)}\right) \). Then the result is obtained by differentiating the univariate Gaussian probability density function with respect to its mean and variance parameters. Indeed we have:

    $$\begin{aligned} d_{\left( \varvec{m}^{(k)},\varSigma ^{(k)}\right) }\left[ g_5\right] (h,H)=&~d_{\varvec{m}^{(k)}}\left[ g_5(\cdot ,\varSigma ^{(k)})\right] (h)+d_{\varSigma ^{(k)}}\left[ g_5(\varvec{m}^{(k)},\cdot )\right] (H) \end{aligned}$$
  • \(g_{6}\) gives the mean and the covariance of \(\varvec{Z}^{(k)}_{-i}|Z_i=0\). We have:

    $$\begin{aligned} \left( \varvec{m}^{(k)}_{|i},\varSigma ^{(k)}_{|i} \right) =g_{6}\left( \varvec{m}^{(k)},\varSigma ^{(k)}\right) =\left( \varvec{m}^{(k)}_{-i}-\frac{m^{(k)}_i}{\varSigma _{ii}^{(k)}}\varvec{\varSigma }_{-i,i}^{(k)}, \varSigma _{-i,-i}^{(k)}-\frac{1}{\varSigma ^{(k)}_{ii}}\varvec{\varSigma }_{-i,i}^{(k)}\varvec{\varSigma }_{-i,i}^{(k)\top }\right) \end{aligned}$$
    $$\begin{aligned} d_{\left( \varvec{m}^{(k)},\varSigma ^{(k)}\right) }\left[ g_{6}\right] (\varvec{h},H) = d_{\varvec{m}^{(k)}}\left[ g_{6}\left( \cdot ,\varSigma ^{(k)}\right) \right] (\varvec{h})+d_{\varSigma ^{(k)}}\left[ g_{6}\right] \left( \varvec{m}^{(k)},\cdot \right) (H), \end{aligned}$$
    $$\begin{aligned}&\text {with : }~~d_{\varvec{m}^{(k)}}\left[ g_{6}\left( \cdot ,\varSigma ^{(k)}\right) \right] (\varvec{h})= \left( \varvec{h}_{-i} -\frac{\varvec{h}_i}{\varSigma _{ii}^{(k)}}\varvec{\varSigma }_{-i,i}^{(k)}, ~0~\right) \nonumber \\&\text {and : }~~d_{\varSigma ^{(k)}}\left[ g_{6}\left( \varvec{m}^{(k)},\cdot \right) \right] (H)= \left( \frac{m^{(k)}_i H_{ii}}{\varSigma _{ii}^{(k)2}}\varvec{\varSigma }_{-i,i}^{(k)}-\frac{m^{(k)}_i}{\varSigma _{ii}^{(k)}}H_{-i,i},\right. \nonumber \\&\left. ~H_{-i,-i}+\frac{H_{ii}}{\varSigma _{ii}^{(k)2}}\varvec{\varSigma }_{-i,i}^{(k)}\varvec{\varSigma }_{-i,i}^{(k)\top }-\frac{1}{\varSigma _{ii}^{(k)}}H_{-i,i}\varvec{\varSigma }_{-i,i}^{(k)\top }-\frac{1}{\varSigma _{ii}^{(k)}}\varvec{\varSigma }_{-i,i}^{(k)}H_{-i,i}^\top \right) \end{aligned}$$
  • \(g_7\) and \(g_8\) : these two functions take a mean vector and a covariance matrix in argument and give a probability in output : \(\varPhi _{q,\varSigma ^{(k)}}\left( -\varvec{m}^{(k)}\right) =g_7\left( \varvec{m}^{(k)},\varSigma ^{(k)}\right) \), \(\varPhi _{q-1,\varSigma ^{(k)}_{|i}}\left( -\varvec{m}^{(k)}_{|i}\right) =g_8\left( \varvec{m}^{(k)}_{|i},\varSigma ^{(k)}_{|i}\right) \) So, for \(\{p,\varGamma ,\varvec{a}\} = \{q,\varSigma ^{(k)},-\varvec{m}^{(k)}\}\) or \(\{q-1,\varSigma ^{(k)}_{|i},-\varvec{m}^{(k)}_{|i}\}\), we face the problem of differentiating a function \(\varPhi : (\varvec{a},\varGamma )\rightarrow \varPhi _{p,\varGamma }(\varvec{a})\), with respect to \((\varvec{a},\varGamma )\in \mathrm {I}\!\mathrm {R}^p\times \mathcal {S}_{++}^p\):

    $$\begin{aligned} d_{(\varvec{a},\varGamma )}\left[ \varPhi \right] (\varvec{h},H)=d_{\varvec{a}}\left[ \varPhi (\cdot ,\varGamma )\right] (\varvec{h})+d_{\varGamma }\left[ \varPhi (\varvec{a},\cdot )\right] (H). \end{aligned}$$

    The first differential of this sum can be written:

    $$\begin{aligned} d_{\varvec{a}}\left[ \varPhi (\cdot ,\varGamma )\right] (\varvec{h}) = \left\langle \left( \frac{\partial }{\partial a_i} \varPhi (\varvec{a},\varGamma )\right) _{1\le i\le p},\varvec{h}\right\rangle , \end{aligned}$$

    with : \(\frac{\partial }{\partial a_i} \varPhi (\varvec{a},\varGamma ) = \int \limits _{-\infty }^{a_1} \!\!\!\ldots \!\!\!\int \limits _{-\infty }^{a_{i-1}} \!\int \limits _{-\infty }^{a_{i+1}}\!\!\!\ldots \!\!\! \int \limits _{-\infty }^{a_p} \varphi _{p,\varGamma }(u_{-i},a_i) \mathrm {d}\varvec{u}_{-i}=\varphi _{1,\varGamma _{ii}} \varPhi _{p-1,\varGamma _{|i}}\left( \varvec{a}_{|i}\right) . \) The last equality is obtained with the identity: \(\forall \varvec{u}\in \mathrm {I}\!\mathrm {R}^q,~ \varphi _{q,\varGamma }(\varvec{u})=\varphi _{1,\varGamma _{ii}}(u_i) \varphi _{p-1,\varGamma _{|i}}(\varvec{u}_{|i}),\) with \(\varvec{u}_{|i}=\varvec{u}_{-i}-\frac{u_i}{\varGamma _{ii}}\varvec{\varGamma }_{-i,i}\) and \(\varGamma _{|i}=\varGamma _{-i,-i}-\frac{1}{\varGamma _{ii}}\varvec{\varGamma }_{-i,i}\varvec{\varGamma }_{-i,i}^\top \). The second differential is:

    $$\begin{aligned}d_{\varGamma }\left[ \varPhi (\varvec{a},\cdot )\right] (H) := \frac{1}{2}\mathrm {tr}\left( H . \left( \frac{\partial \varPhi }{\partial \varGamma _{ij}} (\varvec{a},\varGamma )\right) _{i,j\le p}\right) \nonumber = \frac{1}{2}\mathrm {tr}\left( H . \left( \frac{\partial ^2\varPhi }{\partial a_i\partial a_j}(\varvec{a},\varGamma )\right) _{i,j\le p}\right) \end{aligned}$$

    where : \(\frac{\partial ^2\varPhi }{\partial a_i\partial a_j} (\varvec{a},\varGamma )= \left\{ \begin{array}{ccc} \varphi _{2,\varSigma _{\{i,j\},\{i,j\}}}(x_i,x_j) \varPhi _{p-2,\varSigma _{|ij}}(\varvec{x}_{|{ij}})\text { , if }i\ne j,\nonumber \\ -\frac{x_i}{\varGamma _{ii}} \frac{\partial }{\partial a_i}\varPhi _{\varGamma }(\varvec{a},\varGamma ) - \sum _{\begin{array}{c} j=1\\ j\ne i \end{array}}^p \frac{1}{\varGamma _{ii}}\varGamma _{ij}\frac{\partial ^2}{\partial a_i\partial a_j} \varPhi (\varvec{a},\varGamma )\nonumber . \end{array}\right. \)

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Marmin, S., Chevalier, C., Ginsbourger, D. (2015). Differentiating the Multipoint Expected Improvement for Optimal Batch Design. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham.

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