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Strong mixed-integer programming formulations for trained neural networks


We present strong mixed-integer programming (MIP) formulations for high-dimensional piecewise linear functions that correspond to trained neural networks. These formulations can be used for a number of important tasks, such as verifying that an image classification network is robust to adversarial inputs, or solving decision problems where the objective function is a machine learning model. We present a generic framework, which may be of independent interest, that provides a way to construct sharp or ideal formulations for the maximum of d affine functions over arbitrary polyhedral input domains. We apply this result to derive MIP formulations for a number of the most popular nonlinear operations (e.g. ReLU and max pooling) that are strictly stronger than other approaches from the literature. We corroborate this computationally, showing that our formulations are able to offer substantial improvements in solve time on verification tasks for image classification networks.

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  1. Note that if \(D = \left\{ x \in {\mathbb {R}}^\eta \,\big |\, Ax \leqslant b\right\} \) is polyhedral, then \(z \cdot D = \left\{ x \in {\mathbb {R}}^\eta \,\big | \, Ax \leqslant bz\right\} \).

  2. As is standard in a Benders’ decomposition approach, we can address this by adding a feasibility cut describing the domain of \({\overline{g}}\) (the region where it is finite valued) instead of an optimality cut of the form (10a).

  3. Alternatively, a constructive proof of validity and idealness using Fourier–Motzkin elimination is given in the extended abstract of this work [4, Proposition 1].

  4. In this context, logits are non-normalized predictions of the neural network so that \({\tilde{x}} \in [0,1]^{28 \times 28}\) is predicted to be digit \(i-1\) with probability \(\exp (f({\tilde{x}})_i)/\sum _{j=1}^{10}\exp (f({\tilde{x}})_j)\) [1].

  5. We use cut callbacks in Gurobi to inject separated inequalities into the cut loop. While this offers little control over when the separation procedure is run, it allows us to take advantage of Gurobi’s sophisticated cut management implementation.


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The authors gratefully acknowledge Yeesian Ng and Ondřej Sýkora for many discussions on the topic of this paper, and for their work on the development of the tf.opt package used in the computational experiments.

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An extended abstract version of this paper appeared in [4].

A tight big-M formulation for Max on polyhedral domains

A tight big-M formulation for Max on polyhedral domains

We present a tightened big-M formulation for the maximum of d affine functions over an arbitrary polytope input domain. We can view the formulation as a relaxation of the system in Proposition 4, where we select d inequalities from each of (10a) and (10b): those corresponding to \({\overline{\alpha }}, \underline{\alpha } \in \{ w^1, \ldots , w^d \}\). This subset yields a valid formulation, and we obviate the need for direct separation. This formulation can also be viewed as an application of Proposition 6.2 of Vielma [72], and is similar to the big-M formulations for generalized disjunctive programs of Trespalacios and Grossmann [71].

Proposition 15

Take coefficients N such that, for each \(\ell , k \in [\![d ]\!]\) with \(\ell \ne k\),

$$\begin{aligned} N^{\ell ,k,+}&\geqslant \max _{x^k \in D_{|k}}\{(w^k - w^\ell ) \cdot x^k\} \end{aligned}$$
$$\begin{aligned} N^{\ell ,k,-}&\leqslant \min _{x^k \in D_{|k}}\{(w^k - w^\ell ) \cdot x^k\} , \end{aligned}$$

and \(N^{k,k,+} = N^{k,k,-} = 0\) for all \(k \in [\![d ]\!]\). Then a valid formulation for \({\text {gr}}(\texttt {Max}{} \circ (f^1,\ldots ,f^d); D)\) is:

$$\begin{aligned}&y \leqslant w^\ell \cdot x + \sum _{k=1}^d (N^{\ell ,k,+} + b^k) z_k\quad \forall \ell \in [\![d ]\!] \end{aligned}$$
$$\begin{aligned}&y \geqslant w^\ell \cdot x + \sum _{k=1}^d (N^{\ell ,k,-} + b^k) z_k\quad \forall \ell \in [\![d ]\!] \end{aligned}$$
$$\begin{aligned}&(x,y,z) \in D \times {\mathbb {R}}\times \varDelta ^d \end{aligned}$$
$$\begin{aligned}&z \in \{0,1\}^d \end{aligned}$$

The tightest possible coefficients in (32) can be computed exactly by solving an LP for each pair of input affine functions \(\ell \ne k\). While this might be exceedingly computationally expensive if d is large, it is potentially viable if d is a small fixed constant. For example, the max pooling neuron computes the maximum over a rectangular window in a larger array [32, Sect. 9.3], and is frequently used in image classification architectures. Typically, max pooling units work with a \(2 \times 2\) or a \(3 \times 3\) window, in which case \(d=4\) or \(d=9\), respectively.

In addition, if in practice we observe that if the set \(D_{|k}\) is empty, then we can infer that the neuron is not irreducible as the k-th input function is never the maximum, and we can safely prune it. In particular, if we attempt to compute the coefficients for \(z_k\) and it is proven infeasible, we can prune the k-th function.

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Anderson, R., Huchette, J., Ma, W. et al. Strong mixed-integer programming formulations for trained neural networks. Math. Program. 183, 3–39 (2020).

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  • Mixed-integer programming
  • Formulations
  • Deep learning

Mathematics Subject Classification

  • 90C11