From 0b13581f39edec7d5fec1294847f9d362e372c59 Mon Sep 17 00:00:00 2001 From: mzagorowska <7868389+mzagorowska@users.noreply.github.com> Date: Tue, 23 Jul 2024 14:01:07 +0200 Subject: [PATCH] Typo "acutal" to "actual" Correcting typo "acutal" to "actual" --- docs/src/manual.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/src/manual.md b/docs/src/manual.md index 40f68eb24..57f007219 100644 --- a/docs/src/manual.md +++ b/docs/src/manual.md @@ -104,6 +104,6 @@ In the light of above, DiffOpt differentiates program variables ``x``, ``s``, `` - 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 acutal 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. +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.