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Learn Challenge: Solving a Linear System with LU Decomposition | Linear Algebra Foundations
Mathematics for Data Science

bookChallenge: Solving a Linear System with LU Decomposition

Task

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A student is analyzing a simple network where the flow balance can be represented as a system of linear equations:

Ax⃗=b⃗A \vec{x} = \vec{b}

Where:

  • AA is a 3Γ—33 \times 3 coefficient matrix;
  • bβƒ—\vec{b} is a vector of known quantities;
  • xβƒ—\vec{x} is the vector of unknowns to be determined.

Your goal is to solve for xβƒ—\vec{x} by performing an LU decomposition of matrix AA, followed by forward and backward substitution. Finally, you'll compare your computed result with NumPy’s built-in solver to confirm correctness.

Your task:

  1. Complete the Python code to:
    • Perform LU decomposition by filling in the missing expressions for LL and UU.
    • Implement forward substitution to solve Lyβƒ—=bβƒ—L\vec{y} = \vec{b}.
    • Implement backward substitution to solve Uxβƒ—=yβƒ—U\vec{x} = \vec{y}.
  2. Compare your result with np.linalg.solve() to verify accuracy.

Solution

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SectionΒ 4. ChapterΒ 10
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bookChallenge: Solving a Linear System with LU Decomposition

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Task

Swipe to start coding

A student is analyzing a simple network where the flow balance can be represented as a system of linear equations:

Ax⃗=b⃗A \vec{x} = \vec{b}

Where:

  • AA is a 3Γ—33 \times 3 coefficient matrix;
  • bβƒ—\vec{b} is a vector of known quantities;
  • xβƒ—\vec{x} is the vector of unknowns to be determined.

Your goal is to solve for xβƒ—\vec{x} by performing an LU decomposition of matrix AA, followed by forward and backward substitution. Finally, you'll compare your computed result with NumPy’s built-in solver to confirm correctness.

Your task:

  1. Complete the Python code to:
    • Perform LU decomposition by filling in the missing expressions for LL and UU.
    • Implement forward substitution to solve Lyβƒ—=bβƒ—L\vec{y} = \vec{b}.
    • Implement backward substitution to solve Uxβƒ—=yβƒ—U\vec{x} = \vec{y}.
  2. Compare your result with np.linalg.solve() to verify accuracy.

Solution

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Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 4. ChapterΒ 10
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