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

`lsqlin`	求解约束线性最小二乘问题
`lsqnonneg`	求解非负线性最小二乘问题
`optim.coder.infbound`	Infinite bound support for code generation （自 R2022b 起）
`mldivide, \`	求解关于 x 的线性方程组 Ax = B
`optimwarmstart`	Create warm start object （自 R2021a 起）

优化	在实时编辑器中优化或求解方程（自 R2020b 起）

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.

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.

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.