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Questions and Answers
What is a linear equation?
What is a linear equation?
Any equation that can be written in the form of ax + by = c, where a, b, and c are constants.
What is a system of linear equations?
What is a system of linear equations?
A collection of one or more linear equations involving the same variables.
What is a solution of a linear equation?
What is a solution of a linear equation?
A list of numbers that satisfies a system of linear equations.
What is a solution set?
What is a solution set?
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What makes two linear systems equivalent?
What makes two linear systems equivalent?
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What is a consistent linear system?
What is a consistent linear system?
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What is an inconsistent linear system?
What is an inconsistent linear system?
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What is a matrix?
What is a matrix?
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What is the size of a matrix?
What is the size of a matrix?
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What are the elementary row operations?
What are the elementary row operations?
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Define row equivalent.
Define row equivalent.
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What are two fundamental questions about a system of linear equations?
What are two fundamental questions about a system of linear equations?
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What is a nonzero column or row in a matrix?
What is a nonzero column or row in a matrix?
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What is a leading entry in a matrix?
What is a leading entry in a matrix?
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What are the criteria for a matrix to be in echelon form?
What are the criteria for a matrix to be in echelon form?
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What are the criteria for a matrix to be in reduced echelon form?
What are the criteria for a matrix to be in reduced echelon form?
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True or false: when a matrix is row reduced into another matrix, U, in echelon form, there is only one such U.
True or false: when a matrix is row reduced into another matrix, U, in echelon form, there is only one such U.
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True or false: when a matrix is row reduced into another matrix, U, in reduced row echelon form, there is only one such U.
True or false: when a matrix is row reduced into another matrix, U, in reduced row echelon form, there is only one such U.
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Define pivot position and pivot column.
Define pivot position and pivot column.
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Explain the Row reduction algorithm.
Explain the Row reduction algorithm.
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Define basic variables and free variables.
Define basic variables and free variables.
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What is the Existence and uniqueness theorem?
What is the Existence and uniqueness theorem?
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How to use row reduction to solve a linear system?
How to use row reduction to solve a linear system?
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Suppose a 4 X 7 coefficient matrix for a system of equations has 4 pivots. Is the system consistent? If the system is consistent, how many solutions are there?
Suppose a 4 X 7 coefficient matrix for a system of equations has 4 pivots. Is the system consistent? If the system is consistent, how many solutions are there?
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Study Notes
Linear Equations and Systems
- A linear equation can be expressed in standard linear form.
- A system of linear equations consists of one or more equations that share common variables.
Solutions of Linear Equations
- A solution satisfies a system when substituted, making all equations true.
- Solutions can vary: none, one unique solution, or infinitely many.
Solution Sets
- A solution set encompasses all possible solutions for a linear system.
Equivalent Linear Systems
- Two linear systems are equivalent if they possess the same solution set.
Consistency in Systems
- A consistent linear system has at least one solution.
- An inconsistent system has no solutions, illustrated by non-intersecting parallel lines.
Matrices
- A matrix is a rectangular grid of numbers defined by its rows (m) and columns (n).
- Coefficient matrix includes only coefficients of the system; an augmented matrix includes these plus constants from equations.
Elementary Row Operations
- Basic row operations include replacement, interchange, and scaling.
Row Equivalence
- Two matrices are row equivalent if one can be converted into the other with elementary row operations.
Fundamental Questions
- Key inquiries involve determining the consistency of the system and the uniqueness of any solutions present.
Nonzero Entries
- Nonzero rows and columns must have at least one non-zero component.
Leading Entries
- The leading entry in a matrix row is the first non-zero element from the left.
Echelon Form Criteria
- Echelon form requires that nonzero rows be above all-zero rows and each leading entry must be positioned to the right of the one above it, with all entries below being zeros.
Reduced Echelon Form Criteria
- Additional conditions for reduced echelon form include having leading entries as 1 and ensuring each leading 1 is the only nonzero entry in its column.
Matrix Uniqueness in Forms
- An echelon form matrix (U) is not unique; multiple forms can exist.
- However, a matrix in reduced row echelon form (U) is unique.
Pivots
- A pivot position denotes where a leading 1 resides in reduced echelon form; a pivot column contains this pivot position.
Row Reduction Algorithm
- Essential steps in solving linear systems involve the row reduction algorithm.
Basic and Free Variables
- Basic variables correspond to pivot positions, while free variables are not leading variables in any row.
Existence and Uniqueness Theorem
- This theorem outlines conditions under which systems have solutions, either unique or infinite.
Solving Linear Systems via Row Reduction
- Row reduction can be employed systematically to find solutions to linear equations.
Coefficient Matrix with Pivots
- If a (4 \times 7) coefficient matrix has four pivots, the system is consistent with four basic variables and three free variables, confirming potential for unique solutions.
Studying That Suits You
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Test your knowledge on linear equations with these flashcards from Chapter 1 of Linear Algebra. Each card covers fundamental concepts such as linear equations, systems of equations, and solutions. Perfect for reinforcing your understanding!