Manual

Supported objectives & constraints - scheme 1

For QPTH/OPTNET style backend, the package supports following Function-in-Set constraints:

and the following objective types:

Supported objectives & constraints - scheme 2

For DiffCP/CVXPY style backend, the package supports following Function-in-Set constraints:

and the following objective types:

Creating a differentiable optimizer

You can create a differentiable optimizer over an existing MOI solver by using the diff_optimizer utility.

diff_optimizer(optimizer_constructor)::Optimizer

julia> import DiffOpt, HiGHS

julia> model = DiffOpt.diff_optimizer(HiGHS.Optimizer)
julia> x = model.add_variable(model)
julia> model.add_constraint(model, ...)

Adding new sets and constraints

The DiffOpt Optimizer behaves similarly to other MOI Optimizers and implements the MOI.AbstractOptimizer API.

Projections on cone sets

DiffOpt requires taking projections and finding projection gradients of vectors while computing the jacobians. For this purpose, we use MathOptSetDistances.jl, which is a dedicated package for computing set distances, projections and projection gradients.

Conic problem formulation

Consider a convex conic optimization problem in its primal (P) and dual (D) forms:

\[\begin{split} \begin{array} {llcc} \textbf{Primal Problem} & & \textbf{Dual Problem} & \\ \mbox{minimize} & c^T x \quad \quad & \mbox{minimize} & b^T y \\ \mbox{subject to} & A x + s = b \quad \quad & \mbox{subject to} & A^T y + c = 0 \\ & s \in \mathcal{K} & & y \in \mathcal{K}^* \end{array} \end{split}\]

where

• $x \in R^n$ is the primal variable, $y \in R^m$ is the dual variable, and $s \in R^m$ is the primal slack

variable

• $\mathcal{K} \subseteq R^m$ is a closed convex cone and $\mathcal{K}^* \subseteq R^m$ is the corresponding dual cone

variable

• $A \in R^{m \times n}$, $b \in R^m$, $c \in R^n$ are problem data

In the light of above, DiffOpt differentiates program variables $x$, $s$, $y$ w.r.t pertubations/sensivities in problem data i.e. $dA$, $db$, $dc$. This is achieved via implicit differentiation and matrix differential calculus.

Note that the primal (P) and dual (D) are self-duals of each other. Similarly, for the constraints we support, $\mathcal{K}$ is same in format as $\mathcal{K}^*$.

Reference articles

• Differentiating Through a Cone Program - Akshay Agrawal, Shane Barratt, Stephen Boyd, Enzo Busseti, Walaa M. Moursi, 2019
• A fast and differentiable QP solver for PyTorch. Crafted by Brandon Amos and J. Zico Kolter.
• OptNet: Differentiable Optimization as a Layer in Neural Networks

Backward Pass vector

One possible point of confusion in finding Jacobians is the role of the backward pass vector - above eqn (7), OptNet: Differentiable Optimization as a Layer in Neural Networks. While differentiating convex programs, it is often the case that we don't want to find the actual derivatives, rather we might be interested in computing the product of Jacobians with a backward pass vector, often used in backprop in machine learning/automatic differentiation. This is what happens in scheme 1 of DiffOpt backend.

But, for the conic system (scheme 2), we provide perturbations in conic data (dA, db, dc) to compute pertubations (dx, dy, dz) in input variables. Unlike the quadratic case, these perturbations are actual derivatives, not the product with a backward pass vector. This is an important distinction between the two schemes of differential optimization.