Problem

Constructs a convex optimization problem from an objective and a list of
constraints. Use <code>psolve</code> to solve the problem.

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

Problem function

<dl><dt>objective</dt>
<dd>A <code>Minimize</code> or <code>Maximize</code> object.</dd>
<dt>constraints</dt>
<dd>A list of <code>Constraint</code> objects (e.g., created by
<code>==</code>, <code>&lt;=</code>, <code>&gt;=</code> operators on expressions). Defaults to
an empty list (unconstrained).</dd></dl>

Arguments

<ul>
<li>Problems must contain at least one <code>Variable</code>.
Zero-variable problems (e.g., minimizing a constant) will cause an
internal error in the reduction pipeline.</li>
</ul>

Known limitations

Create an Optimization Problem — Problem

<dl>

<dt>objective</dt>
<dd>A <code>Minimize</code> or <code>Maximize</code> object.</dd>


<dt>constraints</dt>
<dd>A list of <code>Constraint</code> objects (e.g., created by
<code>==</code>, <code>&lt;=</code>, <code>&gt;=</code> operators on expressions). Defaults to
an empty list (unconstrained).</dd>

</dl>

<ul>
<li>Problems must contain at least one <code>Variable</code>.
Zero-variable problems (e.g., minimizing a constant) will cause an
internal error in the reduction pipeline.</li>
</ul>

Problem: Create an Optimization Problem

Description

Usage

Value

Arguments

Known limitations

Examples