Kantorovich theorem

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Short description: About the convergence of Newton's method

The Kantorovich theorem, or Newton–Kantorovich theorem, is a mathematical statement on the semi-local convergence of Newton's method. It was first stated by Leonid Kantorovich in 1948.[1][2] It is similar to the form of the Banach fixed-point theorem, although it states existence and uniqueness of a zero rather than a fixed point.[3]

Newton's method constructs a sequence of points that under certain conditions will converge to a solution x of an equation f(x)=0 or a vector solution of a system of equation F(x)=0. The Kantorovich theorem gives conditions on the initial point of this sequence. If those conditions are satisfied then a solution exists close to the initial point and the sequence converges to that point.[1][2]

Assumptions

Let X⊂ℝn be an open subset and F:X⊂ℝn→ℝn a differentiable function with a Jacobian F′(𝐱) that is locally Lipschitz continuous (for instance if F is twice differentiable). That is, it is assumed that for any x∈X there is an open subset U⊂X such that x∈U and there exists a constant L>0 such that for any 𝐱,𝐲∈U

‖F′(𝐱)−F′(𝐲)‖≤L‖𝐱−𝐲‖

holds. The norm on the left is the operator norm. In other words, for any vector 𝐯∈ℝn the inequality

‖F′(𝐱)(𝐯)−F′(𝐲)(𝐯)‖≤L‖𝐱−𝐲‖‖𝐯‖

must hold.

Now choose any initial point 𝐱0∈X. Assume that F′(𝐱0) is invertible and construct the Newton step 𝐡0=−F′(𝐱0)−1F(𝐱0).

The next assumption is that not only the next point 𝐱1=𝐱0+𝐡0 but the entire ball B(𝐱1,‖𝐡0‖) is contained inside the set X. Let M be the Lipschitz constant for the Jacobian over this ball (assuming it exists).

As a last preparation, construct recursively, as long as it is possible, the sequences (𝐱k)k, (𝐡k)k, (αk)k according to

𝐡k=−F′(𝐱k)−1F(𝐱k)αk=M‖F′(𝐱k)−1‖‖𝐡k‖𝐱k+1=𝐱k+𝐡k.

Statement

Now if α0≤12 then

  1. a solution 𝐱* of F(𝐱*)=0 exists inside the closed ball B¯(𝐱1,‖𝐡0‖) and
  2. the Newton iteration starting in 𝐱0 converges to 𝐱* with at least linear order of convergence.

A statement that is more precise but slightly more difficult to prove uses the roots t∗≤t** of the quadratic polynomial

p(t)=(12L‖F′(𝐱0)−1‖−1)t2−t+‖𝐡0‖,
t∗/**=2‖𝐡0‖1±1−2α0

and their ratio

θ=t*t**=1−1−2α01+1−2α0.

Then

  1. a solution 𝐱* exists inside the closed ball B¯(𝐱1,θ‖𝐡0‖)⊂B¯(𝐱0,t*)
  2. it is unique inside the bigger ball B(𝐱0,t*∗)
  3. and the convergence to the solution of F is dominated by the convergence of the Newton iteration of the quadratic polynomial p(t) towards its smallest root t∗,[4] if t0=0,tk+1=tk−p(tk)p′(tk), then
    ‖𝐱k+p−𝐱k‖≤tk+p−tk.
  4. The quadratic convergence is obtained from the error estimate[5]
    ‖𝐱n+1−𝐱*‖≤θ2n‖𝐱n+1−𝐱n‖≤θ2n2n‖𝐡0‖.

Corollary

In 1986, Yamamoto proved that the error evaluations of the Newton method such as Doring (1969), Ostrowski (1971, 1973),[6][7] Gragg-Tapia (1974), Potra-Ptak (1980),[8] Miel (1981),[9] Potra (1984),[10] can be derived from the Kantorovich theorem.[11]

Generalizations

There is a q-analog for the Kantorovich theorem.[12][13] For other generalizations/variations, see Ortega & Rheinboldt (1970).[14]

Applications

Oishi and Tanabe claimed that the Kantorovich theorem can be applied to obtain reliable solutions of linear programming.[15]

References

  1. ↑ 1.0 1.1 Deuflhard, P. (2004). Newton Methods for Nonlinear Problems. Affine Invariance and Adaptive Algorithms. Springer Series in Computational Mathematics. 35. Berlin: Springer. ISBN 3-540-21099-7. 
  2. ↑ 2.0 2.1 Zeidler, E. (1985). Nonlinear Functional Analysis and its Applications: Part 1: Fixed-Point Theorems. New York: Springer. ISBN 0-387-96499-1. 
  3. ↑ Dennis, John E.; Schnabel, Robert B. (1983). "The Kantorovich and Contractive Mapping Theorems". Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Englewood Cliffs: Prentice-Hall. pp. 92–94. ISBN 0-13-627216-9. https://books.google.com/books?id=ksvJTtJCx9cC&pg=PA92. 
  4. ↑ Ortega, J. M. (1968). "The Newton-Kantorovich Theorem". Amer. Math. Monthly 75 (6): 658–660. doi:10.2307/2313800. 
  5. ↑ Gragg, W. B.; Tapia, R. A. (1974). "Optimal Error Bounds for the Newton-Kantorovich Theorem". SIAM Journal on Numerical Analysis 11 (1): 10–13. doi:10.1137/0711002. Bibcode: 1974SJNA...11...10G. 
  6. ↑ Ostrowski, A. M. (1971). "La method de Newton dans les espaces de Banach". C. R. Acad. Sci. Paris 27 (A): 1251–1253. 
  7. ↑ Ostrowski, A. M. (1973). Solution of Equations in Euclidean and Banach Spaces. New York: Academic Press. ISBN 0-12-530260-6. 
  8. ↑ Potra, F. A.; Ptak, V. (1980). "Sharp error bounds for Newton's process". Numer. Math. 34: 63–72. doi:10.1007/BF01463998. 
  9. ↑ Miel, G. J. (1981). "An updated version of the Kantorovich theorem for Newton's method". Computing 27 (3): 237–244. doi:10.1007/BF02237981. 
  10. ↑ Potra, F. A. (1984). "On the a posteriori error estimates for Newton's method". Beiträge zur Numerische Mathematik 12: 125–138. 
  11. ↑ Yamamoto, T. (1986). "A method for finding sharp error bounds for Newton's method under the Kantorovich assumptions". Numerische Mathematik 49 (2–3): 203–220. doi:10.1007/BF01389624. 
  12. ↑ Rajkovic, P. M.; Stankovic, M. S.; Marinkovic, S. D. (2003). "On q-iterative methods for solving equations and systems". Novi Sad J. Math 33 (2): 127–137. 
  13. ↑ Rajković, P. M.; Marinković, S. D.; Stanković, M. S. (2005). "On q-Newton–Kantorovich method for solving systems of equations". Applied Mathematics and Computation 168 (2): 1432–1448. doi:10.1016/j.amc.2004.10.035. 
  14. ↑ Ortega, J. M.; Rheinboldt, W. C. (1970). Iterative Solution of Nonlinear Equations in Several Variables. SIAM. OCLC 95021. 
  15. ↑ Oishi, S.; Tanabe, K. (2009). "Numerical Inclusion of Optimum Point for Linear Programming". JSIAM Letters 1: 5–8. doi:10.14495/jsiaml.1.5. 

Further reading

  • John H. Hubbard and Barbara Burke Hubbard: Vector Calculus, Linear Algebra, and Differential Forms: A Unified Approach, Matrix Editions, ISBN 978-0-9715766-3-6 (preview of 3. edition and sample material including Kant.-thm.)
  • Yamamoto, Tetsuro (2001). "Historical Developments in Convergence Analysis for Newton's and Newton-like Methods". in Brezinski, C.; Wuytack, L.. Numerical Analysis : Historical Developments in the 20th Century. North-Holland. pp. 241–263. ISBN 0-444-50617-9.