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Laurent Perron
2021-09-21 10:55:35 +02:00
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docs/python/ortools/sat/python/cp_model.html
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@@ -669,24 +669,32 @@ rather than for solving specific optimization problems.</p>
<span class="sd">&quot;&quot;&quot;Holds an integer linear expression.</span>
<span class="sd"> A linear expression is built from integer constants and variables.</span>
<span class="sd"> For example, x + 2 * (y - z + 1).</span>
<span class="sd"> For example, `x + 2 * (y - z + 1)`.</span>
<span class="sd"> Linear expressions are used in CP-SAT models in two ways:</span>
<span class="sd"> Linear expressions are used in CP-SAT models in constraints and in the</span>
<span class="sd"> objective:</span>
<span class="sd"> * To define constraints. For example</span>
<span class="sd"> * You can define linear constraints as in:</span>
<span class="sd"> model.Add(x + 2 * y &lt;= 5)</span>
<span class="sd"> model.Add(sum(array_of_vars) == 5)</span>
<span class="sd"> ```</span>
<span class="sd"> model.Add(x + 2 * y &lt;= 5)</span>
<span class="sd"> model.Add(sum(array_of_vars) == 5)</span>
<span class="sd"> ```</span>
<span class="sd"> * To define the objective function. For example</span>
<span class="sd"> * In CP-SAT, the objective is a linear expression:</span>
<span class="sd"> model.Minimize(x + 2 * y + z)</span>
<span class="sd"> ```</span>
<span class="sd"> model.Minimize(x + 2 * y + z)</span>
<span class="sd"> ```</span>
<span class="sd"> For large arrays, you can create constraints and the objective</span>
<span class="sd"> from lists of linear expressions or coefficients as follows:</span>
<span class="sd"> * For large arrays, using the LinearExpr class is faster that using the python</span>
<span class="sd"> `sum()` function. You can create constraints and the objective from lists of</span>
<span class="sd"> linear expressions or coefficients as follows:</span>
<span class="sd"> model.Minimize(cp_model.LinearExpr.Sum(expressions))</span>
<span class="sd"> model.Add(cp_model.LinearExpr.ScalProd(expressions, coefficients) &gt;= 0)</span>
<span class="sd"> ```</span>
<span class="sd"> model.Minimize(cp_model.LinearExpr.Sum(expressions))</span>
<span class="sd"> model.Add(cp_model.LinearExpr.ScalProd(expressions, coefficients) &gt;= 0)</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@classmethod</span>
@@ -2898,24 +2906,32 @@ rather than for solving specific optimization problems.</p>
<span class="sd">&quot;&quot;&quot;Holds an integer linear expression.</span>
<span class="sd"> A linear expression is built from integer constants and variables.</span>
<span class="sd"> For example, x + 2 * (y - z + 1).</span>
<span class="sd"> For example, `x + 2 * (y - z + 1)`.</span>
<span class="sd"> Linear expressions are used in CP-SAT models in two ways:</span>
<span class="sd"> Linear expressions are used in CP-SAT models in constraints and in the</span>
<span class="sd"> objective:</span>
<span class="sd"> * To define constraints. For example</span>
<span class="sd"> * You can define linear constraints as in:</span>
<span class="sd"> model.Add(x + 2 * y &lt;= 5)</span>
<span class="sd"> model.Add(sum(array_of_vars) == 5)</span>
<span class="sd"> ```</span>
<span class="sd"> model.Add(x + 2 * y &lt;= 5)</span>
<span class="sd"> model.Add(sum(array_of_vars) == 5)</span>
<span class="sd"> ```</span>
<span class="sd"> * To define the objective function. For example</span>
<span class="sd"> * In CP-SAT, the objective is a linear expression:</span>
<span class="sd"> model.Minimize(x + 2 * y + z)</span>
<span class="sd"> ```</span>
<span class="sd"> model.Minimize(x + 2 * y + z)</span>
<span class="sd"> ```</span>
<span class="sd"> For large arrays, you can create constraints and the objective</span>
<span class="sd"> from lists of linear expressions or coefficients as follows:</span>
<span class="sd"> * For large arrays, using the LinearExpr class is faster that using the python</span>
<span class="sd"> `sum()` function. You can create constraints and the objective from lists of</span>
<span class="sd"> linear expressions or coefficients as follows:</span>
<span class="sd"> model.Minimize(cp_model.LinearExpr.Sum(expressions))</span>
<span class="sd"> model.Add(cp_model.LinearExpr.ScalProd(expressions, coefficients) &gt;= 0)</span>
<span class="sd"> ```</span>
<span class="sd"> model.Minimize(cp_model.LinearExpr.Sum(expressions))</span>
<span class="sd"> model.Add(cp_model.LinearExpr.ScalProd(expressions, coefficients) &gt;= 0)</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@classmethod</span>
@@ -3124,22 +3140,31 @@ rather than for solving specific optimization problems.</p>
<div class="docstring"><p>Holds an integer linear expression.</p>
<p>A linear expression is built from integer constants and variables.
For example, x + 2 * (y - z + 1).</p>
For example, <code>x + 2 * (y - z + 1)</code>.</p>
<p>Linear expressions are used in CP-SAT models in two ways:</p>
<p>Linear expressions are used in CP-SAT models in constraints and in the
objective:</p>
<ul>
<li><p>To define constraints. For example</p>
<p>model.Add(x + 2 * y &lt;= 5)
model.Add(sum(array_of_vars) == 5)</p></li>
<li><p>To define the objective function. For example</p>
<p>model.Minimize(x + 2 * y + z)</p></li>
<li>You can define linear constraints as in:</li>
</ul>
<p>For large arrays, you can create constraints and the objective
from lists of linear expressions or coefficients as follows:</p>
<pre><code>model.Add(x + 2 * y &lt;= 5)
model.Add(sum(array_of_vars) == 5)
</code></pre>
<ul>
<li>In CP-SAT, the objective is a linear expression:</li>
</ul>
<pre><code>model.Minimize(x + 2 * y + z)
</code></pre>
<ul>
<li>For large arrays, using the LinearExpr class is faster that using the python
<code>sum()</code> function. You can create constraints and the objective from lists of
linear expressions or coefficients as follows:</li>
</ul>
<pre><code>model.Minimize(cp_model.LinearExpr.Sum(expressions))
model.Add(cp_model.LinearExpr.ScalProd(expressions, coefficients) &gt;= 0)