线性最小二乘
求解具有边界或线性约束的线性最小二乘问题
在开始求解优化问题之前,您必须选择合适的方法:基于问题或基于求解器。有关详细信息,请参阅首先选择基于问题或基于求解器的方法。
线性最小二乘求解 min||C*x - d||2,可能有边界或线性约束。
对于基于问题的方法,请创建问题变量,然后用这些符号变量表示目标函数和约束。有关基于问题的求解步骤,请参阅基于问题的优化工作流。要求解生成的问题,请使用 solve
。
有关基于求解器的求解步骤,包括定义目标函数和约束,以及选择合适的求解器,请参阅基于求解器的优化问题设置。要求解生成的问题,请使用 lsqlin
;或者,对于非负最小二乘,也可以使用 lsqnonneg
。
函数
实时编辑器任务
优化 | 在实时编辑器中优化或求解方程 (自 R2020b 起) |
主题
基于问题的线性最小二乘
- Shortest Distance to a Plane
Shows how to solve a linear least-squares problem using the problem-based approach. - Nonnegative Linear Least Squares, Problem-Based
Shows how to solve a nonnegative linear least-squares problem using the problem-based approach and several solvers. - Large-Scale Constrained Linear Least-Squares, Problem-Based
Solves an optical deblurring problem using the problem-based approach. - Write Objective Function for Problem-Based Least Squares
Syntax rules for problem-based least squares.
基于求解器的线性最小二乘
- Optimize Live Editor Task with lsqlin Solver
Example showing the Optimize Live Editor task and linear least squares. - Nonnegative Linear Least Squares, Solver-Based
This example shows how to use several algorithms to solve a linear least-squares problem with the bound constraint that the solution is nonnegative. - Jacobian Multiply Function with Linear Least Squares
Example showing how to save memory in a large structured linear least-squares problem. - Warm Start Best Practices
Describes how best to use warm start for speeding repeated solutions. - Large-Scale Constrained Linear Least-Squares, Solver-Based
Solves an optical deblurring problem using the solver-based approach.
代码生成
- 线性最小二乘中的代码生成:背景
为线性最小二乘生成 C 代码的前提条件。 - Generate Code for lsqlin
Example of code generation for linear least squares. - Optimization Code Generation for Real-Time Applications
Explore techniques for handling real-time requirements in generated code.
基于问题的算法
- Write Objective Function for Problem-Based Least Squares
Syntax rules for problem-based least squares. - 基于问题的优化算法
了解优化函数和对象如何求解优化问题。 - Supported Operations for Optimization Variables and Expressions
Explore the supported mathematical and indexing operations for optimization variables and expressions.
算法和选项
- 最小二乘(模型拟合)算法
在仅具有边界或线性约束的情况下,在 n 个维度中最小化平方和。 - 优化选项参考
了解优化选项。