Mathlab and least square problem

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In summary, Mathlab is a high-level programming language and interactive environment commonly used in engineering, mathematics, and science fields for data analysis and modeling. It offers built-in functions to solve the least square problem, which is a mathematical optimization technique used to find the best fit for a set of data points. Mathlab's advantages for solving least square problems include its extensive library of functions and algorithms, efficient computation capabilities, and user-friendly interface. It can also handle large datasets, although the size of the dataset may affect computation time.
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hytuoc
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Does anyone know how accurate is MATLAB in calculating least square solution?
Thanks!
 
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  • #3
A=[1,1,1;-1,3,1;2,3,-2;1,3,2] A is 4X4

b=[4;0;1;2] ; b is 4X1

I calculated x^ (residual of x) to be [1.4127; -.0159; .8889]
r(x^) = b-A*x^ = [1.7143; 0.5714; 0; -1.1429]
Nul(A')=[-1.5;-.5;0;1]
so how accurate is MATLAB in calculating least square solution?
 
  • #4
anyone know the answer to my question above?
 
  • #5
Are you asking how does Matlab choose the solution when you give it an overdetermined system?
 

1. What is Mathlab?

Mathlab is a high-level programming language and interactive environment for numerical computation, data analysis, and visualization. It is commonly used in engineering, mathematics, and science fields for data analysis and modeling.

2. What is the least square problem?

The least square problem is a mathematical optimization technique used to find the best fit for a set of data points by minimizing the sum of the squared differences between the observed values and the predicted values. It is commonly used in regression analysis to find the line of best fit for a set of data.

3. How does Mathlab solve the least square problem?

Mathlab has built-in functions, such as polyfit and lsqcurvefit, that use numerical methods to find the coefficients of the polynomial or curve that best fits the given data points. It also has tools for visualizing the data and the resulting fit.

4. What are the advantages of using Mathlab for least square problems?

Mathlab is a powerful tool for solving least square problems due to its extensive library of functions and algorithms, efficient computation capabilities, and user-friendly interface. It also allows for easy manipulation and visualization of data, making it a popular choice for data analysis and modeling tasks.

5. Can Mathlab handle large datasets for least square problems?

Yes, Mathlab has the ability to handle large datasets for least square problems. It is designed to efficiently handle large amounts of data and has various optimization techniques to improve performance. However, the size of the dataset may affect the computation time, and users may need to optimize their code to improve efficiency.

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