is_dnlp

Tests whether the object follows the Disciplined Nonlinear Programming
(DNLP) rules: smooth representable, i.e. linearizable-convex or
linearizable-concave.

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

is_dnlp function

<dl><dt>x</dt>
<dd>An expression, objective, constraint, or <code>Problem</code>.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Check if an Expression or Problem is DNLP-Compliant — is_dnlp

<dl>

<dt>x</dt>
<dd>An expression, objective, constraint, or <code>Problem</code>.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

is_dnlp: Check if an Expression or Problem is DNLP-Compliant

Description

Usage

Value

Arguments