On the choice of the low-dimensional domain for global optimization via random embeddings


The challenge of taking many variables into account in optimization problems may be overcome under the hypothesis of low effective dimensionality. Then, the search of solutions can be reduced to the random embedding of a low dimensional space into the original one, resulting in a more manageable optimization problem. Specifically, in the case of time consuming black-box functions and when the budget of evaluations is severely limited, global optimization with random embeddings appears as a sound alternative to random search. Yet, in the case of box constraints on the native variables, defining suitable bounds on a low dimensional domain appears to be complex. Indeed, a small search domain does not guarantee to find a solution even under restrictive hypotheses about the function, while a larger one may slow down convergence dramatically. Here we tackle the issue of low-dimensional domain selection based on a detailed study of the properties of the random embedding, giving insight on the aforementioned difficulties. In particular, we describe a minimal low-dimensional set in correspondence with the embedded search space. We additionally show that an alternative equivalent embedding procedure yields simultaneously a simpler definition of the low-dimensional minimal set and better properties in practice. Finally, the performance and robustness gains of the proposed enhancements for Bayesian optimization are illustrated on numerical examples.

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We thank the anonymous reviewers for helpful comments on the earlier version of the paper. Parts of this work have been conducted within the frame of the ReDice Consortium, gathering industrial (CEA, EDF, IFPEN, IRSN, Renault) and academic (Ecole des Mines de Saint-Etienne, INRIA, and the University of Bern) partners around advanced methods for Computer Experiments. M.B. also acknowledges partial support from National Science Foundation grant DMS-1521702.

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A Proofs

A.1 Properties of the convex projection

We begin with two elementary properties of the convex projection onto the hypercube \(\mathcal {X}= [-1,1]^D\):

Property 1

[Tensorization property] \(\forall \mathbf {x}\in \mathbb {R}^D\), \(p_\mathcal {X}\begin{pmatrix} x_1 \\ \ldots \\ x_D \end{pmatrix} = \begin{pmatrix} p_{[-1,1]}(x_1) \\ \ldots \\ p_{[-1,1]}(x_D) \end{pmatrix} .\)

Property 2

[Commutativity with some isometries] Let q be an isometry represented by a diagonal matrix with terms \(\varepsilon _i = \pm 1\), \(1 \le i \le D\). Then, for all \(\mathbf {x}\in \mathbb {R}^D\), \(p_\mathcal {X}(q(\mathbf {x})) = \begin{pmatrix} \varepsilon _1 p_{[-1,1]}(x_1) \\ \ldots \\ \varepsilon _D p_{[-1,1]}(x_D) \end{pmatrix} = q(p_\mathcal {X}(\mathbf {x}))\).

A.2 Proof of Theorem 1


First, note that \(\mathcal {U}\) is a closed set as a finite union of closed sets. Then, let us show that \(p_\mathcal {X}(\mathbf {A}\mathcal {U}) = \mathcal {E}\). Consider \(\mathbf {x}\in \mathcal {E}\), hence \(|x_i| \le 1\) and \(\exists \mathbf {y}\in \mathbb {R}^d\) s.t. \(\mathbf {x}= p_\mathcal {X}(\mathbf {A}\mathbf {y})\). Denote \(\mathbf {b}= \mathbf {A}\mathbf {y}\). We distinguish two cases:

  1. 1.

    More than d components of \(\mathbf {b}\) are in \([-1,1]\). Then there exists a set \(I \subset \{1, \ldots , D \}\) of cardinality d such that \(\mathbf {y}\in \bigcap \nolimits _{i \in I} \mathcal {S}_i = \mathcal {P}_I \subseteq \mathcal {U}\), implying that \(\mathbf {x}\in p_\mathcal {X}(\mathbf {A}\mathcal {U})\).

  2. 2.

    \(0 \le k < d\) components of \(\mathbf {b}\) are in \([-1,1]\). It is enough to consider that \(\mathbf {b}\in [0, +\infty )^D\). Indeed, for any \(\mathbf {x}\in \mathcal {E}\), any \(\mathbf {A}\in \mathcal {A}\), let \(\varvec{\varepsilon }\) be the isometry given by the diagonal \(D \times D\) matrix \(\varvec{\varepsilon }\) with elements \(\pm 1\) such that \(\varvec{\varepsilon } \mathbf {x}\in [0, +\infty )^D\). It follows that \(\varvec{\varepsilon }\mathbf {b}\) is in \([0, +\infty )^D\) too. Denote \(\mathbf {x}' = \varvec{\varepsilon } \mathbf {x}\), \(\mathbf {b}' = \varvec{\varepsilon } \mathbf {b}\) and \(\mathbf {A}' = \varvec{\varepsilon } \mathbf {A}\). Thus if \(\exists \mathbf {u}\in \mathcal {U}\) such that \(\mathbf {x}' = p_\mathcal {X}(\mathbf {b}') = p_\mathcal {X}(\mathbf {A}' \mathbf {u})\), by property 2: \(\varvec{\varepsilon } \mathbf {x}= \varvec{\varepsilon } p_\mathcal {X}(\mathbf {A}\mathbf {u})\) leading to \(\mathbf {x}= p_\mathcal {X}(\mathbf {b}) = p_\mathcal {X}(\mathbf {A}\mathbf {u})\). From now on, we therefore assume that \(b_i \ge 0\), \(1 \le i \le D\). Furthermore, we can assume that \(0 \le b_1 \le \ldots \le b_D\), from a permutation of indices. Hence \(b_i > 1\) if \(i > k\) and \(\mathbf {x}= (x_1 = b_1, \ldots , x_k = b_k, 1, \ldots , 1)^T\). Let \(\mathbf {y}' \in \mathbb {R}^d\) be the solution of \(\mathbf {A}_{1, \ldots ,d} \mathbf {y}' = (b_1, \ldots b_k, 1, \ldots , 1)^T\) (vector of size d). Such a solution exists since \(\mathbf {A}_{1, \ldots ,d}\) is invertible by hypothesis. Then define \(\mathbf {b}' = \mathbf {A}\mathbf {y}'\), \(\mathbf {b}' = (b_1, \ldots , b_k,1, \ldots , 1, b_{d+1}', \ldots , b_D')^T\). We have \(\mathbf {b}' \in \text {Ran}(\mathbf {A})\) and \(\mathbf {y}' \in \mathcal {P}_{1, \ldots , d} \subseteq \mathcal {U}\).

    • If \(\min _{i \in \{d+1, \ldots , D\}}(b_i') \ge 1\), then \(p_\mathcal {X}(\mathbf {b}') = p_\mathcal {X}(\mathbf {b}) = \mathbf {x}\), and thus \(\mathbf {x}= p_\mathcal {X}(\mathbf {A}\mathbf {y}') \in p_\mathcal {X}(\mathbf {A}\mathcal {U})\).

    • Else, the set \(L = \{i \in \mathbb {N}: d+1 \le i \le D \,,\,b'_i < 1\}\) is not empty. Consider \(\mathbf {c}= \lambda \mathbf {b}' + (1-\lambda )\mathbf {b}\), \(\lambda \in ]0,1[\). By linearity, since both \(\mathbf {b}\) and \(\mathbf {b}'\) belong to \(\text {Ran}(\mathbf {A})\), \(\mathbf {c}\in \text {Ran}(\mathbf {A})\).

      • For \(1 \le i \le k\), \(c_i = x_i\).

      • For \(k+1 \le i \le d\), \(c_i = \lambda + (1- \lambda )b_i \ge 1\) since \(b_i > 1\).

      • For \(i \in \{d+1, \ldots , D\} \setminus L\), \(b'_i \ge 1\) and \(b_i > 1\) hence \(c_i \ge 1\).

      • We now focus on the remaining components in L. For all \(i \in L\), we solve \(c_i = 1\), i.e., \(\lambda b'_i + (1-\lambda ) b_i = \lambda (b'_i - b_i) + b_i = 1\). The solution is \(\lambda _i = \frac{b_i-1}{b_i - b'_i}\), with \(b_i - b'_i > 0\) since \(b'_i < 1\). Also \(b_i - 1 > 0\) and \(b_i - 1 < b_i - b'_i\) such that we have \(\lambda _i \in ]0,1[\). Denote \(\lambda ^* = \min _{i \in L} \lambda _i\) and the corresponding index \(i^*\). By construction, \(c_{i^*} = 1\) and \(\forall i \in L\), \(c_i = \lambda ^* (b'_i - b_i) + b_i \ge \lambda _i (b'_i - b_i) + b_i = 1\) since \(\lambda _i \ge \lambda ^*\) and \(b'_i - b_i < 0\).

      To summarize, we can construct \(\mathbf {c}^*\) with \(\lambda ^*\) that has \(k + 1\) components in \([-1,1]\) (the first k and the \(i{*th}\) ones), the others are greater or equal than 1. Moreover, \(\mathbf {c}^* \in \text {Ran}(\mathbf {A})\) and fulfills \(p_\mathcal {X}(\mathbf {c}^*) = p_\mathcal {X}(\mathbf {b}) = \mathbf {x}\) by Property 1. If \(k+1 = d\), this corresponds to case 1 above, otherwise, it is possible to reiterate by taking \(\mathbf {b}= \mathbf {c}\). Hence we have a pre-image of \(\mathbf {x}\) by \(\phi \) in \(\mathcal {U}\).

Thus the surjection property is shown. There remains to show that \(\mathcal {U}\) is the smallest closed set achieving this, along with additional topological properties.

Let us show that any closed set \(\mathcal {Y}\in \mathbb {R}^d\) such that \(p_\mathcal {X}(\mathbf {A}\mathcal {Y}) = \mathcal {E}\) contains \(\mathcal {U}\). To this end, we consider \(\mathcal {U}^* = \bigcup \nolimits _{I \subseteq \{1, \ldots , D\}, |I| = d} \mathring{\mathcal {P}}_I\) with \(\mathring{\mathcal {P}}_I = \left\{ {\mathbf {y}\in \mathbb {R}^d \,,\,\forall i \in I, -1< \mathbf {A}_i \mathbf {y}< 1}\right\} \), the interior of the parallelotopes. We have \({\phi }|_{\mathring{U}}\) bijective. Indeed, all \(\mathbf {x}\in p_\mathcal {X}(\mathbf {A}\mathcal {U}^*)\) have (at least) d components whose absolute value is strictly lower that 1. Without loss of generality, we suppose that they are the d first ones, \(I = \{1, \ldots , d\}\). Then there exists a unique\(\mathbf {y}\in \mathbb {R}^d\) s.t. \(\mathbf {x}= p_\mathcal {X}(\mathbf {A}\mathbf {y})\) because \(\mathbf {x}_I = (\mathbf {A}\mathbf {y})_I = \mathbf {A}_I \mathbf {y}\) has a unique solution with \(\mathbf {A}_I\) is invertible. Since \(\mathcal {Y}\) is in surjection with \(\mathcal {E}\) for \({\phi }|_{\mathcal {Y}}\) and \({\phi }|_{\mathcal {U}^*}\) is bijective, \(\mathcal {U}^* \subseteq \mathcal {Y}\). Additionally, \(\mathcal {Y}\) is a closed set so it must contain the closure \(\mathcal {U}\) of \(\mathcal {U}^*\).

Finally let us prove the topological properties of \(\mathcal {U}\). Recall that parallelotopes \(\mathcal {P}_I\)\((I \subseteq \{1, \ldots , D \})\) are compact convex sets as linear transformations of d-cubes. Thus \(\mathcal {I}= \bigcap \limits _{I \subseteq \{1, \ldots , D \}, |I| = d} \mathcal {P}_I\) is a compact convex set as the intersection of compact convex sets, which is non empty (\(O \in \mathcal {I}\)). It follows that \(\mathcal {U}= \bigcup \limits _{I \subseteq \{1, \ldots , D \}, |I| = d} \mathcal {P}_I\) is compact and connected as a finite union of compact connected sets with a non-empty intersection, i.e., \(\mathcal {I}\). Additionally \(\mathcal {U}\) is star-shaped with respect to any point in \(\mathcal {I}\) (since \(\mathcal {I}\) belongs to all parallelotopes in \(\mathcal {U}\)).\(\square \)

A.3 Proof of Proposition 1


It follows from Definition 2 that \(p_\mathbf {A}(\mathcal {X})\) is a zonotope of center O, obtained from the orthogonal projection of the D-hypercube \(\mathcal {X}\). As such, \(p_\mathbf {A}(\mathcal {X})\) is a convex polytope.

Since \(\mathcal {E}\subset \mathcal {X}\), it is direct that \(p_\mathbf {A}(\mathcal {E}) \subseteq p_\mathbf {A}(\mathcal {X})\).

To prove \(p_\mathbf {A}(\mathcal {X}) \subseteq p_\mathbf {A}(\mathcal {E})\), let us start by vertices. Denote by \(\mathbf {x}\in \mathbb {R}^D\) a vertex of \(p_\mathbf {A}(\mathcal {X})\). If \(\mathbf {x}\in \mathcal {X}\), then \(p_\mathbf {A}(p_\mathcal {X}(\mathbf {x})) = p_\mathbf {A}(\mathbf {x}) = \mathbf {x}\), i.e., \(\mathbf {x}\) has a pre-image in \(\mathcal {E}\) by \(p_\mathbf {A}\). Else, if \(\mathbf {x}\notin \mathcal {X}\), consider the vertex \(\mathbf {v}\) of \(\mathcal {X}\) such that \(p_\mathbf {A}(\mathbf {v}) = \mathbf {x}\). Suppose that \(\mathbf {v}\notin \mathcal {E}\). Let us remark that if \(\mathbf {v}\) is a vertex of \(\mathcal {X}\) such that \(\mathbf {v}\notin \mathcal {E}\), then \(\text {Ran}(\mathbf {A}) \cap H_v = \emptyset \), where \(H_v\) is the open hyper-octant (with strict inequalities) that contains \(\mathbf {v}\). Indeed, if \(\mathbf {x}\in \text {Ran}(\mathbf {A}) \cap H_v\), \(\exists k \in \mathbb {R}^*\) such that \(p_\mathcal {X}(k \mathbf {x}) = \mathbf {v}\), which contradicts \(\mathbf {v}\notin \mathcal {E}\). Denote by \(\mathbf {u}\) the intersection of the line \((O\mathbf {x})\) with \(\mathcal {X}\), since \(\mathbf {x}\notin H_v\), \(\mathbf {u}\notin H_v\) either, hence \(\widehat{\mathbf {x}\mathbf {u}\mathbf {v}} > \pi /2\). Then \(\Vert \mathbf {u}- \mathbf {v}\Vert \le \Vert \mathbf {x}- \mathbf {v}\Vert \), which contradicts \(\mathbf {x}= p_\mathbf {A}(\mathbf {v})\). Hence \(\mathbf {v}\in \mathcal {E}\) and \(\mathbf {x}\) has a pre-image by \(p_\mathbf {A}\) in \(\mathcal {E}\).

Now, suppose \(\exists \mathbf {x}\in p_\mathbf {A}(\mathcal {X})\) such that its pre-image(s) in \(\mathcal {X}\) by \(p_\mathbf {A}\) belong to \(\mathcal {X}\setminus \mathcal {E}\). Denote \(\mathbf {x}' \in p_\mathbf {A}(\mathcal {X})\) the closest vertex of \(p_\mathbf {A}(\mathcal {X})\), which has a pre-image in \(\mathcal {E}\) by \(p_\mathbf {A}\). By continuity of \(p_\mathbf {A}\), there exists \(\mathbf {x}'' \in [\mathbf {x}, \mathbf {x}']\) with pre-image in \((\mathcal {X}\setminus \mathcal {E}) \cap \mathcal {E}= \emptyset \), hence there is a contradiction since \(\mathbf {x}''\) has at least one pre-image. Consequently \(\mathbf {x}\) has at least a pre-image in \(\mathcal {E}\), and \(p_\mathbf {A}(\mathcal {X}) \subseteq p_\mathbf {A}(\mathcal {E})\).\(\square \)

A.4 Proof of Theorem 2


As a preliminary, let us show that \(\forall \mathbf {y}\in \mathcal {Z}\), \(\gamma (\mathbf {y}) \in \mathcal {E}\). Let \(\mathbf {x}_1 \in \mathcal {X}\cap p_\mathbf {A}^{-1}(\mathbf {B}^\top \mathbf {y}) (\ne \emptyset )\). From Proposition 1, \(\mathbf {y}\) also have a pre-image \(\mathbf {x}_2 \in \mathcal {E}\) by \(p_\mathbf {A}\), and denote \(\mathbf {u}\in \text {Ran}(\mathbf {A})\) such that \(p_\mathcal {X}(\mathbf {u}) = \mathbf {x}_2\), i.e., \(\Vert \mathbf {x}_2 - \mathbf {u}\Vert = \min \limits _{\mathbf {x}\in \mathcal {X}} \Vert \mathbf {x}- \mathbf {u}\Vert \). We have \(\Vert \mathbf {x}_1 - \mathbf {u}\Vert ^2 = \Vert \mathbf {x}_1 - \mathbf {B}^\top \mathbf {y}\Vert ^2 + \Vert \mathbf {B}^\top \mathbf {y}- \mathbf {u}\Vert ^2\) and \(\Vert \mathbf {x}_2 - \mathbf {u}\Vert ^2 = \Vert \mathbf {x}_2 - \mathbf {B}^\top \mathbf {y}\Vert ^2 + \Vert \mathbf {B}^\top \mathbf {y}- \mathbf {u}\Vert ^2\) as \(\mathbf {x}_1, \mathbf {x}_2 \in p_\mathbf {A}^{-1}(\mathbf {B}^\top \mathbf {y})\). Then, \(\Vert \mathbf {x}_2 - \mathbf {u}\Vert \le \Vert \mathbf {x}_1 - \mathbf {u}\Vert \Rightarrow \Vert \mathbf {x}_2 - \mathbf {B}^\top \mathbf {y}\Vert \le \Vert \mathbf {x}_1 - \mathbf {B}^\top \mathbf {y}\Vert \) with equality if \(\mathbf {x}_1 = \mathbf {x}_2\), so that \(\gamma (\mathbf {B}^\top \mathbf {y}) \in \mathcal {E}\).

We now proceed by showing that \(\gamma \) defines a bijection from \(\mathcal {Z}\) to \(\mathcal {E}\), with \(\gamma ^{-1} = \mathbf {B}\). First, \(\forall \mathbf {y}\in \mathcal {Z}\), \(\mathbf {B}\gamma (\mathbf {y}) = \mathbf {y}\) by definition of \(\gamma \). It remains to show that, \(\forall \mathbf {x}\in \mathcal {E}\), \(\gamma (\mathbf {B}\mathbf {x}) = \mathbf {x}\). Let \(\mathbf {x}\in \mathcal {E}\), \(\mathbf {u}\in \text {Ran}(\mathbf {A})\) such that \(p_\mathcal {X}(\mathbf {u}) = \mathbf {x}\). Suppose \(\gamma (\mathbf {B}\mathbf {x}) = \mathbf {x}' \in \mathcal {E}\), \(\mathbf {x}' \ne \mathbf {x}\), in particular \(\Vert \mathbf {x}' - \mathbf {B}^\top \mathbf {B}\mathbf {x}\Vert < \Vert \mathbf {x}- \mathbf {B}^\top \mathbf {B}\mathbf {x}\Vert \). Again, \(\mathbf {x}, \mathbf {x}' \in p_\mathbf {A}^{-1}(\mathbf {B}^\top \mathbf {B}\mathbf {x})\), hence \(\Vert \mathbf {x}' - \mathbf {B}^\top \mathbf {B}\mathbf {x}\Vert ^2 + \Vert \mathbf {B}^\top \mathbf {B}\mathbf {x}- \mathbf {u}\Vert ^2 = \Vert \mathbf {x}' - \mathbf {u}\Vert ^2 < \Vert \mathbf {x}- \mathbf {B}^\top \mathbf {B}\mathbf {x}\Vert ^2 + \Vert \mathbf {B}^\top \mathbf {B}\mathbf {x}- \mathbf {u}\Vert ^2 = \Vert \mathbf {x}- \mathbf {u}\Vert ^2\) which contradicts \(\mathbf {x}= p_\mathcal {X}(\mathbf {u})\). Thus \(\gamma (\mathbf {B}\mathbf {x}) = \mathbf {x}\).

\(\mathcal {Z}\) is compact, convex and centrally symmetric from being a zonotope, see Definition 2. Finally, any smaller set than \(\mathcal {Z}\) would not have an image through \(\gamma \) covering \(\mathcal {E}\) entirely, which concludes the proof.\(\square \)

A.5 Proof of Proposition 2


The first part directly follows from the properties of the convex and orthogonal projection. In detail: \(\text {Vol}_d(\mathbf {A}\mathcal {U}) \ge \text {Vol}_d(p_\mathcal {X}(\mathbf {A}\mathcal {U})) = \text {Vol}_d(\mathcal {E}) \ge \text {Vol}_d(p_\mathbf {A}(\mathcal {E})) = \text {Vol}_d(\mathbf {A}\mathcal {Z})\).

For the second part, we need the length of a strip \(\mathcal {S}_i\), i.e., the inter hyperplane distance: \(l_i = 2 / \Vert \mathbf {A}_i \Vert \). Recall that \(\mathbf {B}= \mathbf {A}^\top \), that rows of \(\mathbf {A}\) have equal norm and \(\mathbf {A}\) orthonormal. Then, following the proof of [16, Theorem 1], \(\sum \limits _{j = 1}^d \Vert \mathbf {B}_j \Vert ^2 = d\) (orthonormality) \(= \sum \limits _{i = 1}^D \sum \limits _{j = 1}^d A_{i,j}^2 = \sum \limits _{i = 1}^D \Vert \mathbf {A}_i \Vert ^2 = D \Vert \mathbf {A}_1 \Vert ^2\), hence \(\Vert \mathbf {A}_1 \Vert = \sqrt{d/D}\). As \(\mathcal {Z}\) is enclosed in the d-sphere of radius \(\sqrt{D}\) and the d-sphere of radius \(\sqrt{D/d}\) is enclosed in \(\mathcal {I}\), the result follows from the formula of the volume of a d-sphere of radius \(\rho \): \(\frac{\pi ^{d/2}}{\Gamma (d/2 + 1)} \rho ^d\). \(\square \)

B Main notations

d Low embedding dimension
D Original dimension, \(D \gg d\)
\(\mathbf {A}\) Random embedding matrix of size \(D \times d\)
\(\mathbf {B}\) Transpose of \(\mathbf {A}\) after orthonormalization
\(\mathcal {X}\) Search domain \([-1,1]^D\)
\(\mathcal {Y}\) Low dimensional optimization domain, in \(\mathbb {R}^d\)
\(\mathcal {Z}\) Zonotope \(\mathbf {B}\mathcal {X}\)
\(p_\mathcal {X}\) Convex projection onto \(\mathcal {X}\)
\(p_\mathbf {A}\) Orthogonal projection onto \(\text {Ran}(\mathbf {A})\)
\(\Psi \) Warping function from \(\mathbb {R}^d\) to \(\text {Ran}(\mathbf {A})\)
\(\phi \) Mapping from \(\mathcal {Y}\subset \mathbb {R}^d\) to \(\mathbb {R}^D\)
\(\gamma \) Mapping from \(\mathcal {Z}\subset \mathbb {R}^d\) to \(\mathbb {R}^D\)
\((\mathcal {R})\) Optimization problem for REMBO with mapping \(\phi \)
\((\mathcal {R}')\) Optimization problem for REMBO with mapping \(\gamma \)
\((\mathcal {Q})\) Minimal volume problem definition with mapping \(\phi \)
\((\mathcal {Q}')\) Minimal volume problem definition with mapping \(\gamma \)
\(\mathcal {B}\) Box enclosing \(\mathcal {Z}\)
\(\mathcal {E}\) Image of \(\mathbb {R}^d\) by \(\phi \)
\(\mathcal {S}_i\) Strip associated with the ith row of \(\mathbf {A}\)
\(\mathcal {I}\) Intersection of all strips \(\mathcal {S}_i\)
\(\mathcal {U}\) Union of all intersection of d strips \(\mathcal {S}_i\)
\(\mathcal {P}_I\) Parallelotope associated with strips in the set I

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Binois, M., Ginsbourger, D. & Roustant, O. On the choice of the low-dimensional domain for global optimization via random embeddings. J Glob Optim 76, 69–90 (2020). https://doi.org/10.1007/s10898-019-00839-1

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  • Expensive black-box optimization
  • Low effective dimensionality
  • Zonotope
  • Bayesian optimization