Optimization is showcased as an accessible and powerful tool in this attractive and instructive text. Optimization techniques are presented with mathematical motivation, without extensive proofs, and with illustrative Julia code. This text is appropriate for an undergraduate or other introductory course on optimization. It is also suitable for a practicing engineer who wants a broad first taste of the field, along with access to working code. Compared with similar texts, this book generally includes a broader set of topics, including uncertainty propagation, multiobjective optimization, multidisciplinary optimization, Gaussian process surrogate models, and expression optimization. The authors provide the building blocks of optimization algorithms rather than presenting a deep treatment of optimization theory. This extensive use of Julia is the most noticeable innovation of the book. Functions and code are used to illustrate techniques as diverse as automatic differentiation, particle swarm optimization, and Bayesian Monte Carlo methods. The code is presented entirely in the text of the book and duplicated online in Jupyter notebooks. This extensive use of Julia is the most noticeable innovation of the book. Functions and code are used to illustrate techniques as diverse as automatic differentiation, particle swarm optimization, and Bayesian Monte Carlo methods.

中文翻译：

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优化算法[Bookshelf]

在这份引人入胜且具有启发性的文章中，优化被展示为可访问且功能强大的工具。提出的优化技术具有数学动机，没有大量的证明，并带有说明性的Julia代码。该文本适用于本科或其他有关优化的入门课程。它也适合希望广泛了解该领域并能访问工作代码的实践工程师。与类似的书本相比，本书通常包含更广泛的主题，包括不确定性传播，多目标优化，多学科优化，高斯过程代理模型和表达式优化。作者提供了优化算法的基础，而不是对优化理论进行了深入探讨。朱莉娅的这种广泛使用是本书最引人注目的创新。函数和代码用于说明各种技术，例如自动微分，粒子群优化和贝叶斯蒙特卡洛方法。该代码完全在本书的文本中显示，并在Jupyter笔记本中在线复制。朱莉娅的这种广泛使用是该书中最引人注目的创新。函数和代码用于说明各种技术，例如自动微分，粒子群优化和贝叶斯蒙特卡洛方法。朱莉娅的这种广泛使用是本书最引人注目的创新。函数和代码用于说明各种技术，例如自动微分，粒子群优化和贝叶斯蒙特卡洛方法。朱莉娅的这种广泛使用是本书最引人注目的创新。函数和代码用于说明各种技术，例如自动微分，粒子群优化和贝叶斯蒙特卡洛方法。