Least squares approximation | Linear Algebra | Khan Academy
TLDRThe video script discusses the concept of the least squares solution in the context of linear algebra, focusing on the case where the equation Ax = b has no solution. It explains that the least squares solution, denoted as x-star, aims to minimize the distance between vector b and the column space of matrix A. The script visualizes the column space and introduces the concept of projection onto the column space as the closest vector to b within this space. It then leads to the mathematical formulation involving A transpose and the null space, ultimately presenting the least squares solution as A transpose b, given that A transpose A times x-star equals A transpose b. The video aims to clarify that this method provides the best approximation to the original equation when an exact solution is not possible.
Takeaways
- 📌 The given matrix A is an n-by-k matrix with a corresponding equation Ax = b, where x is in Rk and b is in Rn.
- 🔍 If there is no solution to Ax = b, it implies that vector b is not in the column space of A, meaning no linear combination of A's column vectors can equal b.
- 📈 The concept of least squares is introduced as a method to find the best approximation to the solution when an exact solution does not exist.
- 🎯 The goal of least squares is to minimize the length of the vector (b - A*x_star), where A*x_star represents the closest vector in the column space of A to vector b.
- 🌟 The least squares solution x_star is found by minimizing the squared differences between the elements of b and A*x_star.
- 📊 To find the least squares solution, the video suggests multiplying both sides of the equation Ax = b by A transpose, leading to A transpose * A * x_star = A transpose * b.
- 🔄 The least squares solution is in the column space of A, and the vector (A*x_star - b) is orthogonal to the column space, belonging to the orthogonal complement of the column space.
- 🌐 The orthogonal complement of the column space is the null space of A transpose, which is a key concept in finding the least squares solution.
- 💡 The least squares solution can be seen as the projection of vector b onto the column space of A, which is the closest vector in that space to b.
- 🛠️ In practice, finding the least squares solution often involves computational methods that simplify the process of minimizing the squared differences and dealing with the projection and orthogonal complement.
Q & A
What is the significance of the matrix A being n-by-k in the context of the equation Ax = b?
-The matrix A being n-by-k implies that there are n rows and k columns, where 'n' represents the number of equations and 'k' represents the number of variables. The vector 'x' would have to be a member of Rk, indicating that there are 'k' variables to solve for, and 'b' is a member of Rn, indicating the number of equations we are trying to satisfy.
What does it mean if there is no solution to the equation Ax = b?
-If there is no solution to the equation Ax = b, it means that it is impossible to find a vector 'x' in the space Rk such that when multiplied by matrix 'A', the result equals vector 'b'. In other words, vector 'b' is not in the column space of matrix 'A', and no linear combination of the column vectors of 'A' can produce vector 'b'.
How can you visualize the column space of a matrix?
-The column space of a matrix can be visualized as a subspace in Rn, where 'n' is the number of rows in the matrix. In the context of the script, it might be visualized as a plane or a more general subspace, depending on the properties of the matrix and its column vectors.
What is the least squares estimate or solution?
-The least squares estimate or solution is an approximation that seeks to find a vector 'x-star' such that when multiplied by matrix 'A', the resulting vector is as close as possible to vector 'b'. It is a way to find the best fit solution when an exact solution to Ax = b does not exist.
How is the least squares solution related to the projection of vector 'b' onto the column space of 'A'?
-The least squares solution aims to find a vector 'x-star' such that Ax-star is equal to the projection of vector 'b' onto the column space of 'A'. This projection is the closest vector to 'b' that lies within the column space of 'A', and minimizing the distance between 'b' and this projection results in the least squares solution.
What is the relationship between the least squares solution and the null space of A transpose?
-The least squares solution is related to the null space of A transpose in that the difference between the actual vector 'b' and the least squares solution (Ax-star) lies in the null space of A transpose. This means that the residual vector (b - Ax-star) is orthogonal to the column space of 'A'.
How can you find the least squares solution to the equation Ax = b?
-To find the least squares solution, you can set up the equation A transpose times Ax = A transpose b and then solve for 'x'. The least squares solution 'x-star' is given by the equation (A transpose A) times x-star = A transpose b, which minimizes the squared error between the actual vector 'b' and the approximated vector Ax-star.
What is the significance of minimizing the length of b - A times x-star?
-Minimizing the length of b - A times x-star is significant because it represents the smallest possible distance between the target vector 'b' and the closest point achievable within the column space of 'A'. This minimization is the core idea behind the least squares approximation, leading to the best possible fit under the given constraints.
How does the concept of orthogonal complement relate to the least squares solution?
-The orthogonal complement of the column space of 'A' is the set of all vectors orthogonal to every vector in the column space. The residual vector (b - Ax-star) from the least squares solution lies in this orthogonal complement, signifying that the least squares error is minimized and the residual is orthogonal to the column space.
What is the role of A transpose in finding the least squares solution?
-A transpose plays a crucial role in the least squares solution by being part of the equation A transpose times Ax = A transpose b, which is used to find 'x-star'. It helps in transforming the original equation into a form that allows us to find the least squares solution by solving this new equation.
Why is the least squares solution useful in practice?
-The least squares solution is useful in practice because it provides a way to approximate solutions when an exact solution is not possible or practical to obtain. It minimizes the error, offering the best fit under the given constraints, which is particularly valuable in fields like data fitting, statistics, and machine learning.
Outlines
📌 Introduction to Matrix Equations and Solutions
The paragraph begins with an introduction to matrix equations, specifically focusing on an n-by-k matrix A and the equation Ax = b. It explains that x must be a member of Rk due to the dimensions of A and b. The speaker then discusses the implications of there being no solution to the equation, expanding on what it means for b to not be in the column space of A. The paragraph further explores visualizing the column space of A and the position of b in relation to it, using a geometrical approach to illustrate the concepts.
📝 Least Squares Estimation and Minimization
This paragraph delves into the concept of least squares estimation as a method to find an approximate solution when an exact solution to Ax = b does not exist. It introduces the idea of minimizing the length of the difference between b and A times an approximate solution, referred to as x-star. The explanation includes a mathematical breakdown of how to calculate this difference and the motivation behind the least squares estimate. The paragraph aims to provide a clear understanding of the least squares solution and its significance in dealing with unsolvable equations.
🔍 Relationship Between Projection and Least Squares Solution
The third paragraph establishes a connection between the projection of a vector onto a subspace and the least squares solution. It explains that the closest vector to b in the column space of A is the projection of b onto this space. The speaker describes the orthogonality of the residual vector (Ax-star - b) to the column space and how this relates to the null space of A transpose. The paragraph presents a mathematical derivation that leads to the least squares solution, emphasizing the importance of A transpose A and A transpose b in this process.
🌟 Simplifying the Least Squares Solution
In the final paragraph, the focus is on simplifying the process of finding the least squares solution. The speaker suggests a method involving the multiplication of both sides of the equation Ax = b by A transpose, which results in a new equation that always has a solution. This solution, as explained, is the least squares solution to the original equation. The paragraph highlights the practicality of this approach and sets the stage for further exploration in subsequent videos, emphasizing the abstract nature of the concept and its potential usefulness.
Mindmap
Keywords
💡Matrix
💡Linear Equation
💡Column Space
💡Least Squares
💡Projection
💡Null Space
💡Transpose
💡Orthogonal
💡Linear Combination
💡Augmented Matrix
Highlights
Exploring the concept of matrix equations and their solutions, specifically when there is no solution to Ax = b.
The necessity of x being a member of Rk due to the n-by-k matrix A and the vector b being a member of Rn.
The visualization of the column space of matrix A and the concept of b not being in the column space, indicating the impossibility of the equation's direct solution.
The introduction of the least squares solution as an approximation when a direct solution is not possible.
The mathematical formulation of the least squares problem: minimizing the length of b - A * x_star.
The explanation of how the difference between the desired vector b and the column space results in an orthogonal vector.
The relationship between the least squares solution and the projection of b onto the column space of A.
The derivation of the least squares solution through the equation A * x_star = projection of b, and its connection to the column space.
The orthogonality condition for the least squares solution: A * x_star - b is orthogonal to the column space of A.
The identification of A * x_star - b as a member of the null space of A transpose, leading to the least squares solution.
The transformation of the original equation Ax = b into A^T * Ax = A^T * b for finding the least squares solution.
The least squares solution is found by solving the equation A^T * A * x_star = A^T * b.
The significance of the least squares solution in minimizing the error between the approximation and the desired vector b.
The potential practical applications and usefulness of the least squares solution in various fields, hinting at its abstract but valuable nature.
Transcripts
Browse More Related Video
The Big Picture of Linear Algebra
Inverse matrices, column space and null space | Chapter 7, Essence of linear algebra
Complex, Hermitian, and Unitary Matrices
Order, Dimension, Rank, Nullity, Null Space, Column Space of a matrix
The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)
21. Eigenvalues and Eigenvectors
5.0 / 5 (0 votes)
Thanks for rating: