lecture approximation

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1. 1▶Lecture 26(B): Approximation & Taylor Polynomials in Rn5. Foundations for OptimizationNotes
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2. 2▶Lecture 26(A): Approximation & Taylor Polynomials in Rn5. Foundations for OptimizationNotes
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3. 3▶Lecture 25(B): Derivatives5. Foundations for OptimizationNotes
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4. 4▶Lecture 25(A): Derivatives5. Foundations for OptimizationNotes
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5. 5▶Lecture 24(B): Approximation and Taylor Polynomials, with example5. Foundations for OptimizationNotes
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6. 6▶Lecture 24(A): Approximation and Taylor Polynomials5. Foundations for OptimizationNotes
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7. 7▶Lecture 29(C): Quadratic Forms: Determinant conditions for definiteness5. Foundations for OptimizationNotes
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8. 8▶Lecture 29(B): Quadratic Forms: Determinant conditions for definiteness5. Foundations for OptimizationNotes
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9. 9▶Lecture 29(A): Quadratic Forms: Necessary & sufficient conditions for definiteness5. Foundations for OptimizationNotes
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10. 10▶Lecture 28: Quadratic Forms: Definite and semidefinite forms5. Foundations for OptimizationNotes
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11. 11▶Lecture 24 11/11: Online Algorithms: PagingKarger SkoltechNotes
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12. 12▶Lecture 22 11/04 Approximation Algorithms: Linear Programming RelaxationsKarger SkoltechNotes
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13. 13▶Lecture 21 11/01 Approximation Algorithms: RelaxationsKarger SkoltechNotes
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14. 14▶Lecture 20 10/30 Polynomial Approximation SchemesKarger SkoltechNotes
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15. 15▶Lecture 19 10/28 Approximation AlgorithmsKarger SkoltechNotes
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16. 16▶Lecture 18 10/25 Linear Programming: Interior PointKarger SkoltechNotes
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17. 17▶Lecture 17 10/23 Linear Programming: Simplex AlgorithmKarger SkoltechNotes
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18. 18▶Lecture 16 10/21 Linear Programming Duality ExamplesKarger SkoltechNotes
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19. 19▶Lecture 15 10/18 Linear Programming DualityKarger SkoltechNotes
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20. 20▶Lecture 14 10/16 Linear ProgrammingKarger SkoltechNotes
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