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    <title>optimization on Ciência Programada</title>
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    <description>Recent content in optimization on Ciência Programada</description>
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    <managingEditor>contato[@]cienciaprogramada.com.br (Francisco Bustamante)</managingEditor>
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    <copyright>© 2026 Francisco Bustamante</copyright>
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      <title>Optimization with SciPy</title>
      <link>https://cienciaprogramada.com/2026/10/optimization-scipy/</link>
      <pubDate>Wed, 28 Oct 2026 00:00:00 +0000</pubDate>
      <author>contato[@]cienciaprogramada.com.br (Francisco Bustamante)</author>
      <guid>https://cienciaprogramada.com/2026/10/optimization-scipy/</guid>
      <description>Differentiating and setting the result to zero solves textbook optimization. With several variables, constraints, or a function without a tractable derivative, that calculation breaks down. We will see minimize, SLSQP and bounds from scipy.optimize, local minima, and global search with differential_evolution.</description>
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      <title>Optimization Problems with Python</title>
      <link>https://cienciaprogramada.com/2022/04/optimization-problems-python/</link>
      <pubDate>Tue, 05 Apr 2022 00:00:00 +0000</pubDate>
      <author>contato[@]cienciaprogramada.com.br (Francisco Bustamante)</author>
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      <description>Engineers and scientists are always trying to get the most performance out of their equipment and projects at the lowest cost. This search gives rise to so-called optimization problems. In this article, we will see how to use Python, with the help of the SymPy package, to solve them.</description>
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