Calculus of variations Information
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Calculus 

The calculus of variations is a field of mathematical analysis that uses variations, which are small changes in functions and functionals, to find maxima and minima of functionals: mappings from a set of functions to the real numbers.^{ [a]} Functionals are often expressed as definite integrals involving functions and their derivatives. Functions that maximize or minimize functionals may be found using the Euler–Lagrange equation of the calculus of variations.
A simple example of such a problem is to find the curve of shortest length connecting two points. If there are no constraints, the solution is a straight line between the points. However, if the curve is constrained to lie on a surface in space, then the solution is less obvious, and possibly many solutions may exist. Such solutions are known as geodesics. A related problem is posed by Fermat's principle: light follows the path of shortest optical length connecting two points, which depends upon the material of the medium. One corresponding concept in mechanics is the principle of least/stationary action.
Many important problems involve functions of several variables. Solutions of boundary value problems for the Laplace equation satisfy the Dirichlet's principle. Plateau's problem requires finding a surface of minimal area that spans a given contour in space: a solution can often be found by dipping a frame in a solution of soap suds. Although such experiments are relatively easy to perform, their mathematical interpretation is far from simple: there may be more than one locally minimizing surface, and they may have nontrivial topology.
History
The calculus of variations may be said to begin with Newton's minimal resistance problem in 1687, followed by the brachistochrone curve problem raised by Johann Bernoulli (1696).^{ [2]} It immediately occupied the attention of Jakob Bernoulli and the Marquis de l'Hôpital, but Leonhard Euler first elaborated the subject, beginning in 1733. Lagrange was influenced by Euler's work to contribute significantly to the theory. After Euler saw the 1755 work of the 19yearold Lagrange, Euler dropped his own partly geometric approach in favor of Lagrange's purely analytic approach and renamed the subject the calculus of variations in his 1756 lecture Elementa Calculi Variationum.^{ [3]}^{ [4]}^{ [1]}
Legendre (1786) laid down a method, not entirely satisfactory, for the discrimination of maxima and minima. Isaac Newton and Gottfried Leibniz also gave some early attention to the subject.^{ [5]} To this discrimination Vincenzo Brunacci (1810), Carl Friedrich Gauss (1829), Siméon Poisson (1831), Mikhail Ostrogradsky (1834), and Carl Jacobi (1837) have been among the contributors. An important general work is that of Sarrus (1842) which was condensed and improved by Cauchy (1844). Other valuable treatises and memoirs have been written by Strauch (1849), Jellett (1850), Otto Hesse (1857), Alfred Clebsch (1858), and Carll (1885), but perhaps the most important work of the century is that of Weierstrass. His celebrated course on the theory is epochmaking, and it may be asserted that he was the first to place it on a firm and unquestionable foundation. The 20th and the 23rd Hilbert problem published in 1900 encouraged further development.^{ [5]}
In the 20th century David Hilbert, Oskar Bolza, Gilbert Ames Bliss, Emmy Noether, Leonida Tonelli, Henri Lebesgue and Jacques Hadamard among others made significant contributions.^{ [5]} Marston Morse applied calculus of variations in what is now called Morse theory.^{ [6]} Lev Pontryagin, Ralph Rockafellar and F. H. Clarke developed new mathematical tools for the calculus of variations in optimal control theory.^{ [6]} The dynamic programming of Richard Bellman is an alternative to the calculus of variations.^{ [7]}^{ [8]}^{ [9]}^{ [b]}
Extrema
The calculus of variations is concerned with the maxima or minima (collectively called extrema) of functionals. A functional maps functions to scalars, so functionals have been described as "functions of functions." Functionals have extrema with respect to the elements of a given function space defined over a given domain. A functional is said to have an extremum at the function if has the same sign for all in an arbitrarily small neighborhood of ^{ [c]} The function is called an extremal function or extremal.^{ [d]} The extremum is called a local maximum if everywhere in an arbitrarily small neighborhood of and a local minimum if there. For a function space of continuous functions, extrema of corresponding functionals are called strong extrema or weak extrema, depending on whether the first derivatives of the continuous functions are respectively all continuous or not.^{ [11]}
Both strong and weak extrema of functionals are for a space of continuous functions but strong extrema have the additional requirement that the first derivatives of the functions in the space be continuous. Thus a strong extremum is also a weak extremum, but the converse may not hold. Finding strong extrema is more difficult than finding weak extrema.^{ [12]} An example of a necessary condition that is used for finding weak extrema is the Euler–Lagrange equation.^{ [13]}^{ [e]}
Euler–Lagrange equation
Finding the extrema of functionals is similar to finding the maxima and minima of functions. The maxima and minima of a function may be located by finding the points where its derivative vanishes (i.e., is equal to zero). The extrema of functionals may be obtained by finding functions for which the functional derivative is equal to zero. This leads to solving the associated Euler–Lagrange equation.^{ [f]}
Consider the functional
 are constants,
 is twice continuously differentiable,
 is twice continuously differentiable with respect to its arguments and
If the functional attains a local minimum at and is an arbitrary function that has at least one derivative and vanishes at the endpoints and then for any number close to 0,
The term is called the variation of the function and is denoted by ^{ [1]}^{ [g]}
Substituting for in the functional the result is a function of
Taking the total derivative of where and are considered as functions of rather than yields
Therefore,
According to the fundamental lemma of calculus of variations, the part of the integrand in parentheses is zero, i.e.
In general this gives a secondorder ordinary differential equation which can be solved to obtain the extremal function The Euler–Lagrange equation is a necessary, but not sufficient, condition for an extremum A sufficient condition for a minimum is given in the section Variations and sufficient condition for a minimum.
Example
In order to illustrate this process, consider the problem of finding the extremal function which is the shortest curve that connects two points and The arc length of the curve is given by
The Euler–Lagrange equation will now be used to find the extremal function that minimizes the functional
Since does not appear explicitly in the first term in the Euler–Lagrange equation vanishes for all and thus,
Thus
Beltrami's identity
In physics problems it may be the case that meaning the integrand is a function of and but does not appear separately. In that case, the Euler–Lagrange equation can be simplified to the Beltrami identity^{ [16]}
The intuition behind this result is that, if the variable is actually time, then the statement implies that the Lagrangian is timeindependent. By Noether's theorem, there is an associated conserved quantity. In this case, this quantity is the Hamiltonian, the Legendre transform of the Lagrangian, which (often) coincides with the energy of the system. This is (minus) the constant in Beltrami's identity.
Euler–Poisson equation
If depends on higherderivatives of that is, if
Du BoisReymond's theorem
The discussion thus far has assumed that extremal functions possess two continuous derivatives, although the existence of the integral requires only first derivatives of trial functions. The condition that the first variation vanishes at an extremal may be regarded as a weak form of the Euler–Lagrange equation. The theorem of Du BoisReymond asserts that this weak form implies the strong form. If has continuous first and second derivatives with respect to all of its arguments, and if
Lavrentiev phenomenon
Hilbert was the first to give good conditions for the Euler–Lagrange equations to give a stationary solution. Within a convex area and a positive thrice differentiable Lagrangian the solutions are composed of a countable collection of sections that either go along the boundary or satisfy the Euler–Lagrange equations in the interior.
However Lavrentiev in 1926 showed that there are circumstances where there is no optimum solution but one can be approached arbitrarily closely by increasing numbers of sections. The Lavrentiev Phenomenon identifies a difference in the infimum of a minimization problem across different classes of admissible functions. For instance the following problem, presented by Manià in 1934:^{ [18]}
Clearly, minimizes the functional, but we find any function gives a value bounded away from the infimum.
Examples (in onedimension) are traditionally manifested across and but Ball and Mizel^{ [19]} procured the first functional that displayed Lavrentiev's Phenomenon across and for There are several results that gives criteria under which the phenomenon does not occur  for instance 'standard growth', a Lagrangian with no dependence on the second variable, or an approximating sequence satisfying Cesari's Condition (D)  but results are often particular, and applicable to a small class of functionals.
Connected with the Lavrentiev Phenomenon is the repulsion property: any functional displaying Lavrentiev's Phenomenon will display the weak repulsion property.^{ [20]}
Functions of several variables
For example, if denotes the displacement of a membrane above the domain in the plane, then its potential energy is proportional to its surface area:
Dirichlet's principle
It is often sufficient to consider only small displacements of the membrane, whose energy difference from no displacement is approximated by
The difficulty with this reasoning is the assumption that the minimizing function u must have two derivatives. Riemann argued that the existence of a smooth minimizing function was assured by the connection with the physical problem: membranes do indeed assume configurations with minimal potential energy. Riemann named this idea the Dirichlet principle in honor of his teacher Peter Gustav Lejeune Dirichlet. However Weierstrass gave an example of a variational problem with no solution: minimize
Generalization to other boundary value problems
A more general expression for the potential energy of a membrane is
The preceding reasoning is not valid if vanishes identically on In such a case, we could allow a trial function where is a constant. For such a trial function,
Eigenvalue problems
Both onedimensional and multidimensional eigenvalue problems can be formulated as variational problems.
Sturm–Liouville problems
The Sturm–Liouville eigenvalue problem involves a general quadratic form
The next smallest eigenvalue and eigenfunction can be obtained by minimizing under the additional constraint
The variational problem also applies to more general boundary conditions. Instead of requiring that vanish at the endpoints, we may not impose any condition at the endpoints, and set
Eigenvalue problems in several dimensions
Eigenvalue problems in higher dimensions are defined in analogy with the onedimensional case. For example, given a domain with boundary in three dimensions we may define
Applications
Optics
Fermat's principle states that light takes a path that (locally) minimizes the optical length between its endpoints. If the coordinate is chosen as the parameter along the path, and along the path, then the optical length is given by
After integration by parts of the first term within brackets, we obtain the Euler–Lagrange equation
The light rays may be determined by integrating this equation. This formalism is used in the context of Lagrangian optics and Hamiltonian optics.
Snell's law
There is a discontinuity of the refractive index when light enters or leaves a lens. Let
The factor multiplying is the sine of angle of the incident ray with the axis, and the factor multiplying is the sine of angle of the refracted ray with the axis. Snell's law for refraction requires that these terms be equal. As this calculation demonstrates, Snell's law is equivalent to vanishing of the first variation of the optical path length.
Fermat's principle in three dimensions
It is expedient to use vector notation: let let be a parameter, let be the parametric representation of a curve and let be its tangent vector. The optical length of the curve is given by
Note that this integral is invariant with respect to changes in the parametric representation of The Euler–Lagrange equations for a minimizing curve have the symmetric form
It follows from the definition that satisfies
Therefore, the integral may also be written as
This form suggests that if we can find a function whose gradient is given by then the integral is given by the difference of at the endpoints of the interval of integration. Thus the problem of studying the curves that make the integral stationary can be related to the study of the level surfaces of In order to find such a function, we turn to the wave equation, which governs the propagation of light. This formalism is used in the context of Lagrangian optics and Hamiltonian optics.
Connection with the wave equation
The wave equation for an inhomogeneous medium is
We may look for solutions in the form
In that case, satisfies
These equations for solution of a firstorder partial differential equation are identical to the Euler–Lagrange equations if we make the identification
We conclude that the function is the value of the minimizing integral as a function of the upper end point. That is, when a family of minimizing curves is constructed, the values of the optical length satisfy the characteristic equation corresponding the wave equation. Hence, solving the associated partial differential equation of first order is equivalent to finding families of solutions of the variational problem. This is the essential content of the Hamilton–Jacobi theory, which applies to more general variational problems.
Mechanics
In classical mechanics, the action, is defined as the time integral of the Lagrangian, The Lagrangian is the difference of energies,
The conjugate momenta are defined by
Further applications
Further applications of the calculus of variations include the following:
 The derivation of the catenary shape
 Solution to Newton's minimal resistance problem
 Solution to the brachistochrone problem
 Solution to the tautochrone problem
 Solution to isoperimetric problems
 Calculating geodesics
 Finding minimal surfaces and solving Plateau's problem
 Optimal control
 Analytical mechanics, or reformulations of Newton's laws of motion, most notably Lagrangian and Hamiltonian mechanics;
 Geometric optics, especially Lagrangian and Hamiltonian optics;
 Variational method (quantum mechanics), one way of finding approximations to the lowest energy eigenstate or ground state, and some excited states;
 Variational Bayesian methods, a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning;
 Variational methods in general relativity, a family of techniques using calculus of variations to solve problems in Einstein's general theory of relativity;
 Finite element method is a variational method for finding numerical solutions to boundaryvalue problems in differential equations;
 Total variation denoising, an image processing method for filtering high variance or noisy signals.
Variations and sufficient condition for a minimum
Calculus of variations is concerned with variations of functionals, which are small changes in the functional's value due to small changes in the function that is its argument. The first variation^{ [k]} is defined as the linear part of the change in the functional, and the second variation^{ [l]} is defined as the quadratic part.^{ [22]}
For example, if is a functional with the function as its argument, and there is a small change in its argument from to where is a function in the same function space as then the corresponding change in the functional is^{ [m]}
The functional is said to be differentiable if
The functional is said to be twice differentiable if
The second variation is said to be strongly positive if
Using the above definitions, especially the definitions of first variation, second variation, and strongly positive, the following sufficient condition for a minimum of a functional can be stated.
Sufficient condition for a minimum:
The functional has a minimum at if its first variation at and its second variation is strongly positive at ^{ [30]} ^{ [q]}^{ [r]}
See also
 First variation
 Isoperimetric inequality
 Variational principle
 Variational bicomplex
 Fermat's principle
 Principle of least action
 Infinitedimensional optimization
 Finite element method
 Functional analysis
 Ekeland's variational principle
 Inverse problem for Lagrangian mechanics
 Obstacle problem
 Perturbation methods
 Young measure
 Optimal control
 Direct method in calculus of variations
 Noether's theorem
 De Donder–Weyl theory
 Variational Bayesian methods
 Chaplygin problem
 Nehari manifold
 Hu–Washizu principle
 Luke's variational principle
 Mountain pass theorem
 Category:Variational analysts
 Measures of central tendency as solutions to variational problems
 Stampacchia Medal
 Fermat Prize
 Convenient vector space
Notes
 ^ Whereas elementary calculus is about infinitesimally small changes in the values of functions without changes in the function itself, calculus of variations is about infinitesimally small changes in the function itself, which are called variations.^{ [1]}
 ^ See Harold J. Kushner (2004): regarding Dynamic Programming, "The calculus of variations had related ideas (e.g., the work of Caratheodory, the HamiltonJacobi equation). This led to conflicts with the calculus of variations community."
 ^ The neighborhood of is the part of the given function space where over the whole domain of the functions, with a positive number that specifies the size of the neighborhood.^{ [10]}
 ^ Note the difference between the terms extremal and extremum. An extremal is a function that makes a functional an extremum.
 ^ For a sufficient condition, see section Variations and sufficient condition for a minimum.
 ^ The following derivation of the Euler–Lagrange equation corresponds to the derivation on pp. 184–185 of Courant & Hilbert (1953).^{ [14]}
 ^ Note that and are evaluated at the same values of which is not valid more generally in variational calculus with nonholonomic constraints.
 ^ The product is called the first variation of the functional and is denoted by Some references define the first variation differently by leaving out the factor.
 ^ As a historical note, this is an axiom of Archimedes. See e.g. Kelland (1843).^{ [15]}
 ^ The resulting controversy over the validity of Dirichlet's principle is explained by Turnbull.^{ [21]}
 ^ The first variation is also called the variation, differential, or first differential.
 ^ The second variation is also called the second differential.
 ^ Note that and the variations below, depend on both and The argument has been left out to simplify the notation. For example, could have been written ^{ [23]}
 ^ A functional is said to be linear if and where are functions and is a real number.^{ [24]}
 ^ For a function that is defined for where and are real numbers, the norm of is its maximum absolute value, i.e. ^{ [25]}
 ^ A functional is said to be quadratic if it is a bilinear functional with two argument functions that are equal. A bilinear functional is a functional that depends on two argument functions and is linear when each argument function in turn is fixed while the other argument function is variable.^{ [27]}

^ For other sufficient conditions, see in
Gelfand & Fomin 2000,
 Chapter 5: "The Second Variation. Sufficient Conditions for a Weak Extremum" – Sufficient conditions for a weak minimum are given by the theorem on p. 116.
 Chapter 6: "Fields. Sufficient Conditions for a Strong Extremum" – Sufficient conditions for a strong minimum are given by the theorem on p. 148.
 ^ One may note the similarity to the sufficient condition for a minimum of a function, where the first derivative is zero and the second derivative is positive.
References
 ^ ^{a} ^{b} Courant & Hilbert 1953, p. 184
 ^ Gelfand, I. M.; Fomin, S. V. (2000). Silverman, Richard A. (ed.). Calculus of variations (Unabridged repr. ed.). Mineola, New York: Dover Publications. p. 3. ISBN 9780486414485.
 ^ ^{a} ^{b} Thiele, Rüdiger (2007). "Euler and the Calculus of Variations". In Bradley, Robert E.; Sandifer, C. Edward (eds.). Leonhard Euler: Life, Work and Legacy. Elsevier. p. 249. ISBN 9780080471297.
 ^ Goldstine, Herman H. (2012). A History of the Calculus of Variations from the 17th through the 19th Century. Springer Science & Business Media. p. 110. ISBN 9781461381068.
 ^ ^{a} ^{b} ^{c} van Brunt, Bruce (2004). The Calculus of Variations. Springer. ISBN 9780387402475.
 ^ ^{a} ^{b} Ferguson, James (2004). "Brief Survey of the History of the Calculus of Variations and its Applications". arXiv: math/0402357.
 ^ Dimitri Bertsekas. Dynamic programming and optimal control. Athena Scientific, 2005.
 ^ Bellman, Richard E. (1954). "Dynamic Programming and a new formalism in the calculus of variations". Proc. Natl. Acad. Sci. 40 (4): 231–235. Bibcode: 1954PNAS...40..231B. doi: 10.1073/pnas.40.4.231. PMC 527981. PMID 16589462.
 ^ "Richard E. Bellman Control Heritage Award". American Automatic Control Council. 2004. Retrieved 20130728.
 ^ Courant, R; Hilbert, D (1953). Methods of Mathematical Physics. Vol. I (First English ed.). New York: Interscience Publishers, Inc. p. 169. ISBN 9780471504474.
 ^ Gelfand & Fomin 2000, pp. 12–13
 ^ Gelfand & Fomin 2000, p. 13
 ^ Gelfand & Fomin 2000, pp. 14–15
 ^ Courant, R.; Hilbert, D. (1953). Methods of Mathematical Physics. Vol. I (First English ed.). New York: Interscience Publishers, Inc. ISBN 9780471504474.
 ^ Kelland, Philip (1843). Lectures on the principles of demonstrative mathematics. p. 58 – via Google Books.
 ^ Weisstein, Eric W. "Euler–Lagrange Differential Equation". mathworld.wolfram.com. Wolfram. Eq. (5).
 ^ Kot, Mark (2014). "Chapter 4: Basic Generalizations". A First Course in the Calculus of Variations. American Mathematical Society. ISBN 9781470414955.
 ^ Manià, Bernard (1934). "Sopra un esempio di Lavrentieff". Bollenttino dell'Unione Matematica Italiana. 13: 147–153.
 ^ Ball & Mizel (1985). "Onedimensional Variational problems whose Minimizers do not satisfy the EulerLagrange equation". Archive for Rational Mechanics and Analysis. 90 (4): 325–388. Bibcode: 1985ArRMA..90..325B. doi: 10.1007/BF00276295. S2CID 55005550.
 ^ Ferriero, Alessandro (2007). "The Weak Repulsion property". Journal de Mathématiques Pures et Appliquées. 88 (4): 378–388. doi: 10.1016/j.matpur.2007.06.002.
 ^ Turnbull. "Riemann biography". UK: U. St. Andrew.
 ^ Gelfand & Fomin 2000, pp. 11–12, 99
 ^ Gelfand & Fomin 2000, p. 12, footnote 6
 ^ Gelfand & Fomin 2000, p. 8
 ^ Gelfand & Fomin 2000, p. 6
 ^ Gelfand & Fomin 2000, pp. 11–12
 ^ Gelfand & Fomin 2000, pp. 97–98
 ^ Gelfand & Fomin 2000, p. 99
 ^ Gelfand & Fomin 2000, p. 100
 ^ Gelfand & Fomin 2000, p. 100, Theorem 2
Further reading
 Benesova, B. and Kruzik, M.: "Weak Lower Semicontinuity of Integral Functionals and Applications". SIAM Review 59(4) (2017), 703–766.
 Bolza, O.: Lectures on the Calculus of Variations. Chelsea Publishing Company, 1904, available on Digital Mathematics library. 2nd edition republished in 1961, paperback in 2005, ISBN 9781418182014.
 Cassel, Kevin W.: Variational Methods with Applications in Science and Engineering, Cambridge University Press, 2013.
 Clegg, J.C.: Calculus of Variations, Interscience Publishers Inc., 1968.
 Courant, R.: Dirichlet's principle, conformal mapping and minimal surfaces. Interscience, 1950.
 Dacorogna, Bernard: " Introduction" Introduction to the Calculus of Variations, 3rd edition. 2014, World Scientific Publishing, ISBN 9781783265510.
 Elsgolc, L.E.: Calculus of Variations, Pergamon Press Ltd., 1962.
 Forsyth, A.R.: Calculus of Variations, Dover, 1960.
 Fox, Charles: An Introduction to the Calculus of Variations, Dover Publ., 1987.
 Giaquinta, Mariano; Hildebrandt, Stefan: Calculus of Variations I and II, SpringerVerlag, ISBN 9783662032787 and ISBN 9783662062012
 Jost, J. and X. LiJost: Calculus of Variations. Cambridge University Press, 1998.
 Lebedev, L.P. and Cloud, M.J.: The Calculus of Variations and Functional Analysis with Optimal Control and Applications in Mechanics, World Scientific, 2003, pages 1–98.
 Logan, J. David: Applied Mathematics, 3rd edition. WileyInterscience, 2006
 Pike, Ralph W. "Chapter 8: Calculus of Variations". Optimization for Engineering Systems. Louisiana State University. Archived from the original on 20070705.
 Roubicek, T.: " Calculus of variations". Chap.17 in: Mathematical Tools for Physicists. (Ed. M. Grinfeld) J. Wiley, Weinheim, 2014, ISBN 9783527411887, pp. 551–588.
 Sagan, Hans: Introduction to the Calculus of Variations, Dover, 1992.
 Weinstock, Robert: Calculus of Variations with Applications to Physics and Engineering, Dover, 1974 (reprint of 1952 ed.).
External links
 Variational calculus. Encyclopedia of Mathematics.
 calculus of variations. PlanetMath.
 Calculus of Variations. MathWorld.
 Calculus of variations. Example problems.
 Mathematics  Calculus of Variations and Integral Equations. Lectures on YouTube.
 Selected papers on Geodesic Fields. Part I, Part II.