backward

Differentiates through the solution map of <code>problem</code>: populates
the <code>gradient</code> slot of each <code>Parameter</code> with the sensitivity of
a scalar-valued function of the variables (defaulting to the
sum-of-x loss; override per variable by setting
<code>gradient(variable) &lt;-</code> before calling) with respect to that
parameter. Mirrors <code>cvxpy.Problem.backward()</code>.

An object-oriented modeling language for disciplined
convex programming (DCP) as described in Fu, Narasimhan, and Boyd
(2020, <doi:10.18637/jss.v094.i14>). It allows the user to
formulate convex optimization problems in a natural way following
mathematical convention and DCP rules. The system analyzes the
problem, verifies its convexity, converts it into a canonical
form, and hands it off to an appropriate solver to obtain the
solution. This version uses the S7 object system for improved
performance and maintainability.

Anqi Fu

CVXR

Disciplined Convex Optimization

Balasubramanian Narasimhan

Steven Diamond

John Miller

Stephen Boyd

backward function

<dl><dt>problem</dt>
<dd>A solved <code>Problem</code>.</dd></dl>

Arguments

Compute the gradient of a solution with respect to Parameters — backward

<dl>

<dt>problem</dt>
<dd>A solved <code>Problem</code>.</dd>

</dl>

backward: Compute the gradient of a solution with respect to Parameters

Description

Usage

Value

Arguments

Details

See Also