solving system of linear equationsspeech.ee.ntu.edu.tw/~tlkagk/courses/la_2019/lecture...solving...
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Solving System of Linear Equations
Hung-yi Lee
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Reference
• Textbook: Chapter 1.3, 1.4
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九章算術
置上禾三秉,中禾二秉,下禾一秉,實三十九斗,於右方。中、左禾列如右方。以右行上禾遍乘中行而以直除。又乘其次,亦以直除。然以中行中禾不盡者遍乘左行而以直除。左方下禾不盡者,上為法,下為實。實即下禾之實。求中禾,以法乘中行下實,而除下禾之實。餘如中禾秉數而一,即中禾之實。求上禾亦以法乘右行下實,而除下禾、中禾之實。餘如上禾秉數而一,即上禾之實。實皆如法,各得一斗。
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算法統宗
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Equivalent
• Two systems of linear equations are equivalent if they have exactly the same solution set.
ቊ3𝑥1 +𝑥2 = 10𝑥1 −3𝑥2 = 0
ቊ𝑥1 = 3
𝑥2 = 1
Solution set: 31
Solution set: 31
equivalent
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Equivalent
• Applying the following three operations on a system of linear equations will produce an equivalent one.
• 1. Interchange
• 2. Scaling
• 3. Row Addition
ቊ3𝑥1 +𝑥2 = 10𝑥1 −3𝑥2 = 0
ቊ𝑥1 −3𝑥2 = 03𝑥1 +𝑥2 = 10
ቊ3𝑥1 +𝑥2 = 10𝑥1 −3𝑥2 = 0 X(-3)
ቊ3𝑥1 +𝑥2 = 10−3𝑥1 +9𝑥2 = 0
ቊ3𝑥1 +𝑥2 = 10𝑥1 −3𝑥2 = 0 X(-3)
ቊ10𝑥2 = 10
𝑥1 −3𝑥2 = 0
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Solving system of linear equation
• Two systems of linear equations are equivalent if they have exactly the same solution set.
• Strategy:
We know how to transform the given system of linear equations into another equivalent system of linear equations.
We do it again and again until the system of linear equation is so simple that we know its answer at a glance.
ቊ𝑥1 −3𝑥2 = 03𝑥1 +𝑥2 = 10
ቊ𝑥1 −3𝑥2 = 0
10𝑥2 = 10ቊ𝑥1 = 3
𝑥2 = 1ቊ𝑥1 −3𝑥2 = 0
𝑥2 = 1
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Augmented Matrix
• a system of linear equation
coefficient matrix
𝐴 =
𝑎11 𝑎12𝑎21 𝑎22
⋯ 𝑎1𝑛⋯ 𝑎2𝑛
⋮ ⋮𝑎𝑚1 𝑎𝑚2
⋱ ⋮⋯ 𝑎𝑚𝑛
m x n𝑥 =
𝑥1𝑥2⋮𝑥𝑛
𝑏 =
𝑏1𝑏2⋮𝑏𝑚
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Augmented Matrix
• a system of linear equation
augmented matrix
m x n m x 1
m x (n+1)
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Solving system of linear equation
• Two systems of linear equations are equivalent if they have exactly the same solution set.
• Strategy of solving:
ቊ𝑥1 −3𝑥2 = 03𝑥1 +𝑥2 = 10
ቊ𝑥1 −3𝑥2 = 0
10𝑥2 = 10ቊ𝑥1 = 3
𝑥2 = 1ቊ𝑥1 −3𝑥2 = 0
𝑥2 = 1
1 −3 03 1 10
1 −3 00 10 10
1 −3 00 1 1
1 0 30 1 1
1. Interchange any two rows of the matrix
2. Multiply every entry of some row by the same nonzero scalar
3. Add a multiple of one row of the matrix to another row
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Solving system of linear equation
equivalent
elementary row operations
Ax = b
A’=[ A b ]
A complex system of linear equations
……A’’ A’’’ R=[ R b ]
Rx = b
A simple system of linear equations
1. Interchange any two rows of the matrix
2. Multiply every entry of some row by the same nonzero scalar
3. Add a multiple of one row of the matrix to another row
reduced row echelon form
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Reduced Row Echelon Form
• A system of linear equations is easily solvable if its augmented matrix is in reduced row echelon form
• Row Echelon Form
1. Each nonzero row lies above every zero row
2. The leading entries arein echelon form
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Reduced Row Echelon Form
• A system of linear equations is easily solvable if its augmented matrix is in reduced row echelon form
• Row Echelon Form
1. Each nonzero row lies above every zero row
No zero rows
2. The leading entries arein echelon form
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Reduced Row Echelon Form
• A system of linear equations is easily solvable if its augmented matrix is in reduced row echelon form
• Reduced Row Echelon Form
1-2 The matrix is in row echelon form
3. The columns containing the leading entries are standard vectors.
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Reduced Row Echelon Form
• A system of linear equations is easily solvable if its augmented matrix is in reduced row echelon form
• Reduced Row Echelon Form
1-2 The matrix is in row echelon form
3. The columns containing the leading entries are standard vectors.
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RREF is unique
• A matrix can be transformed into multiple REF by row operation, but only one RREF
REF
RREF
1 −20 30 0
−1 33 −61 3
1 −20 30 0
−1 30 −153 9
1 −20 30 0
−1 33 −63 9
REF
REF
P577
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Reduced Row Echelon Form
A R
Leading Entry
The pivot positions of A are (1,1), (2,3) and (3,4).
The pivot columns of A are 1st, 3rd and 4th columns.
Pivot 中樞
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Reduced Row Echelon Form
• A system of linear equations is easily solvable if its augmented matrix is in reduced row echelon form
If RREF looks
like [ I b ]
Example 1. Unique Solution
unique solution
bx1 x2 x3
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Free variables
With free variables, there are
infinitely many solutions.
Example 2. Infinite Solution
bx1 x2 x3 x4 x5
Basic variables
Parametric Representation:
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Reduced Row Echelon Form
• Example 3. No Solution
inconsistent
bx1 x2 x3
When an augmented matrix contains a row in which
the only nonzero entry lies in the last column
The corresponding system of
linear equations has no solution (inconsistent).
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Reduced Row Echelon Form
• RREF of a matrix is unique.
• Gaussian elimination: an algorithm for finding the reduced row echelon form of a matrix.
Original augmented matrix
A row echelon formThe reduced row
echelon form
Please refer to the steps of Gaussian Elimination in the textbook by yourself.
Elementary row operations
Elementary row operations
http://www.dougbabcock.com/matrix.phphttp://www.ams.org/notices/201106/rtx110600782p.pdf
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Example 1
1-2
3
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Example 1
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Example 1
-1
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Example 1
-2
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Example 1
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Example 1
bx1 x2 x3 x4 x5
𝑥1𝑥2𝑥3𝑥4𝑥5
=
−5 − 2𝑥2 + 𝑥5𝑥2−3
2 − 𝑥5𝑥5
= 𝑥2
−21000
+𝑥5
100−11
+
−50−320
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𝑥1𝑥2𝑥3𝑥4𝑥5
=
−5 − 2𝑥2 + 𝑥5𝑥2−3
2 − 𝑥5𝑥5
= 𝑥2
−21000
+𝑥5
100−11
+
−50−320
𝑥2 = −5
2−1
2𝑥1 +
1
2𝑥5
𝑥1 𝑓𝑟𝑒𝑒
𝑥1𝑥2𝑥3𝑥4𝑥5
=
𝑥1−5/2 − 1/2𝑥1 + 1/2𝑥5
−32 − 𝑥5𝑥5
= 𝑥1
1−1/2000
+𝑥5
01/20−11
+
0−5/2−320
1 -1
-81
3-1
-1-8
-8 1
3-1
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1 00 1
−3 02 0
0 00 0
0 10 0
2 00 2
−6 04 0
0 00 0
0 20 0
Example 2
• Find the RREF of
𝑅 =
1 00 1
−3 02 0
0 00 0
0 10 0
𝑅
2𝑅× −2
1 00 1
−3 02 0
0 00 0
0 10 0
0 00 0
0 00 0
0 00 0
0 00 0
𝑅
𝑶
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Example 3
• Find the RREF of 𝑅 =
1 00 1
−3 02 0
0 00 0
0 10 0
𝑅 2𝑅𝑅 −𝑅
𝑅 2𝑅𝑅 −𝑅
𝑅 2𝑅𝑶 −3𝑅
𝑅 𝑶𝑶 −3𝑅
𝑅 𝑶𝑶 𝑅
1 00 1
−3 02 0
0 00 0
0 10 0
0 00 0
0 00 0
0 00 0
0 00 0
0 00 0
0 00 0
0 00 0
0 00 0
1 00 1
−3 02 0
0 00 0
0 10 0
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Checking Independence
A set of n vectors {a1, a2, , an} is linear dependent
𝐴𝑥 = 𝟎 have non-zero solution
Linear independent or not?
Given a vector set, {a1, a2, , an}, if there exists any ai that is a linear combination of other vectors
Given a vector set, {a1, a2, , an}, there exists scalars x1, x2, , xn, that are not all zero, such that x1a1 + x2a2 + + xnan
= 0.
matrix A
vector x
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Checking Independence
1 12 01 1
1 14 21 3
𝑥1𝑥2𝑥3𝑥4
=000
RREF
𝐴𝑥 = 𝟎 have non-zero solution or not
A
x1 x2 x3 x4 x1 x2 x3 x4
Linear independent or not?
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Checking Independence
RREF
x1 x2 x3 x4 x1 x2 x3 x4
𝑥1 + 2𝑥3 = 0
𝑥2 − 𝑥3 = 0
𝑥4 = 0 𝑥4 = 0
𝑥1 = −2𝑥3𝑥2 = 𝑥3
𝑥3 𝑖𝑠 𝑓𝑟𝑒𝑒
𝑥1𝑥2𝑥3𝑥4
=
−2𝑥3𝑥3𝑥30
= 𝑥3
−2110
setting x3 = 1 𝑥1𝑥2𝑥3𝑥4
=
−2110