1
Q
Definition
Convex cone.
A
2
Q
Lemma 9.1 (Hyperplane separation for cones)
A
3
Q
Theorem 9.2 (Farkas’ Lemma)
A
4
Q
Corollary 9.3
there exists x s.t. Ax <= b iff.
A
5
Q
Corollary 9.4
<c, x> <= \delta iff.
A
6
Q
Theorem 9.5 (Duality theorem for linear programming)
A
7
Q
Lemma 9.7
When is supremum attained in polyhedron.
A
MA265 MMM3
flashcards
Decks in class (19)
# Cards
-
1. Intro to Optimisation4
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2. Prerequisites8
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3. Convexity I7
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4. Convexity II3
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6. Iterative Algorithms II5
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7. Newton's Method5
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8. Linear Programming I: Polyhedra5
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9. Linear Programming II: Duality7
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10. Lagrange Duality6
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11. KKT Optimality Conditions4
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13. Introduction to Approximation Theory2
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14. Polynomial Approximation in the max-norm I4
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15. Polynomial Approximation in the max-norm II5
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16. Polynomial Approximation in the 2-norm I2
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17. Polynomial Approximation in the 2-norm II3
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18. Approximation by Rational Functions1
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19. Trigonometric Approximation and Fourier Series7
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20. Fourier Series II4
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23. Best-fit Subspaces and SVD II6
