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constraint.html

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No license found. Starting COPT with size limitations for non-commercial use
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;pyoptinterface._src.core_ext.ConstraintIndex at 0x7f16c8df4830&gt;
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;pyoptinterface._src.core_ext.ConstraintIndex at 0x7eff4ca5c4f0&gt;
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;pyoptinterface._src.core_ext.ConstraintIndex at 0x7eff4ca5ccd0&gt;
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;pyoptinterface._src.core_ext.ConstraintIndex at 0x7eff4ca5cd30&gt;
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container.html

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RHS [1e+00, 1e+00]
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Iteration Objective NullspaceDim
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0 20.000001 0 0.00s
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Model status : Optimal

examples/economic_dispatch.html

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QP ASM iterations: 108
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Objective value : 1.2684000000e+04
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P-D objective error : 1.6461008020e-06
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HiGHS run time : 0.01
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HiGHS run time : 0.00
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TerminationStatusCode.OPTIMAL
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Objective value: 12684.0
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Model status : Optimal
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Simplex iterations: 86
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Objective value : 1.3740213571e+04
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P-D objective error : 1.6699127419e-06
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Objective value: 13740.213571429404
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Objective value : 1.3740213571e+04
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P-D objective error : 1.6699127419e-06
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examples/optimal_control_rocket.html

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Number of equality constraint Jacobian evaluations = 38
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Number of inequality constraint Jacobian evaluations = 0
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Number of Lagrangian Hessian evaluations = 37
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Total CPU secs in IPOPT (w/o function evaluations) = 0.351
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Total CPU secs in IPOPT (w/o function evaluations) = 0.354
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Total CPU secs in NLP function evaluations = 0.010
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EXIT: Optimal Solution Found.
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expression.html

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Wall time: 741 μs
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Wall time: 716 μs
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Wall time: 15.5 ms
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getting_started.html

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Bound [0e+00, 0e+00]
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Model status : Optimal
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infeasibility.html

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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>No license found. LP size is limited to 10000 variables and 10000 constraints
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<p>This code snippet creates an infeasible model with two constraints and finds the IIS of the model. Obviously, the constraints are contradictory because <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">+</span> <span class="pre">2</span> <span class="pre">*</span> <span class="pre">y</span> <span class="pre">&lt;=</span> <span class="pre">1</span></code> and <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">+</span> <span class="pre">y</span> <span class="pre">&gt;=</span> <span class="pre">5</span></code> cannot be satisfied at the same time when <code class="docutils literal notranslate"><span class="pre">x</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> are non-negative. The optimizer will detect that the model is infeasible and return the IIS, which is the set of constraints that cause the infeasibility. We can query whether a constraint is in the IIS by calling <code class="docutils literal notranslate"><span class="pre">get_constraint_attribute</span></code> with the <code class="docutils literal notranslate"><span class="pre">ConstraintAttribute.IIS</span></code> attribute.</p>

model.html

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nonlinear.html

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EXIT: Optimal Solution Found.

numpy.html

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Termination status: TerminationStatusCode.OPTIMAL
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Model status : Optimal
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