
Nonlinear Programming (非线性规划)
“Nonlinear programming” 是一个复合名词,通常用于数学、优化和计算机科学领域。它指的是在约束条件下,目标函数为非线性的优化问题。该词没有明显的形容词和名词的不同含义,主要用作名词。
词语辨析
“Nonlinear programming” 的核心是“非线性”,表示目标函数或约束条件不是线性的。它与“线性规划”(linear programming)相对,后者的目标函数和约束条件都是线性的。
词汇扩充
相关词汇包括:
- Objective function (目标函数)
- Constraints (约束条件)
- Optimization (优化)
- Feasible region (可行区域)
- Gradient (梯度)
近义词
- Nonlinear optimization (非线性优化)
反义词
- Linear programming (线性规划)
词典引用
根据柯林斯词典和牛津词典,“nonlinear programming” 定义为一种数学方法,旨在解决具有非线性关系的优化问题。
用法示例
Nonlinear programming is used in various fields such as economics, engineering, and operations research, where complex relationships exist.
非线性规划被广泛应用于经济学、工程学和运筹学等领域,尤其在存在复杂关系的情况下。
Many real-world problems require nonlinear programming techniques to find optimal solutions.
许多现实问题需要非线性规划技术来寻找最佳解决方案。
In nonlinear programming, the objective function and constraints can be represented by non-linear equations.
在非线性规划中,目标函数和约束可以用非线性方程表示。
The feasible region in nonlinear programming may not be convex, unlike in linear programming.
非线性规划中的可行区域可能不是凸的,这与线性规划不同。
To solve a nonlinear programming problem, one might use gradient descent methods.
为了解决非线性规划问题,可以使用梯度下降方法。
One challenge in nonlinear programming is to ensure that the solution found is a global optimum.
非线性规划中的一个挑战是确保找到的解是全局最优解。
Software tools for nonlinear programming can help automate the optimization process.
用于非线性规划的软件工具可以帮助自动化优化过程。
In practice, nonlinear programming problems can be much harder to solve than their linear counterparts.
在实践中,非线性规划问题往往比线性问题更难解决。
Understanding the theory behind nonlinear programming is essential for effective application.
理解非线性规划背后的理论对于有效应用至关重要。
Many optimization problems in machine learning involve nonlinear programming techniques.
机器学习中的许多优化问题涉及非线性规划技术。
The constraints in a nonlinear programming model can be both equality and inequality types.
非线性规划模型中的约束条件可以是等式和不等式类型。
Researchers often develop new algorithms to improve nonlinear programming efficiency.
研究人员常常开发新算法以提高非线性规划的效率。
In finance, nonlinear programming can be used for portfolio optimization.
在金融领域,非线性规划可用于投资组合优化。
Understanding convexity is crucial when working with nonlinear programming.
理解凸性在处理非线性规划时至关重要。
Nonlinear programming allows for more flexibility in modeling real-world problems.
非线性规划在建模现实问题时提供了更多的灵活性。
Successful application of nonlinear programming can lead to significant cost savings.
成功应用非线性规划可以带来显著的成本节约。
In engineering design, nonlinear programming can optimize structures under complex loads.
在工程设计中,非线性规划可以在复杂负载下优化结构。
Nonlinear programming techniques are essential for solving problems in operations research.
非线性规划技术对于解决运筹学中的问题至关重要。