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In quantum mechanics, the variational method is one way of finding approximations to the lowest energy eigenstate or ground state, and some excited states. This allows calculating approximate wavefunctions such as molecular orbitals. [1] The basis for this method is the variational principle. [2] [3]
The calculus of variations began with the work of Isaac Newton, such as with Newton's minimal resistance problem, which he formulated and solved in 1685, and later published in his Principia in 1687, [2] which was the first problem in the field to be formulated and correctly solved, [2] and was also one of the most difficult problems tackled by variational methods prior to the twentieth century.
Within chemistry, a Job plot, otherwise known as the method of continuous variation or Job's method, is a method used in analytical chemistry to determine the stoichiometry of a binding event. The method is named after Paul Job and is also used in instrumental analysis and advanced chemical equilibrium texts and research articles.
According to S. Ilanko, [2] citing Richard Courant, both Lord Rayleigh and Walther Ritz independently conceived the idea of utilizing the equivalence between boundary value problems of partial differential equations on the one hand and problems of the calculus of variations on the other hand for numerical calculation of the solutions, by ...
For example, the problem of determining the shape of a hanging chain suspended at both ends—a catenary—can be solved using variational calculus, and in this case, the variational principle is the following: The solution is a function that minimizes the gravitational potential energy of the chain.
As a result, the algorithm requires an upper bound on the cost that can be obtained from extending a partial solution, and this upper bound should be as small as possible. A variation of this approach called Hansen's method uses interval methods. [5] It inherently implements rectangular constraints.
Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning.They are typically used in complex statistical models consisting of observed variables (usually termed "data") as well as unknown parameters and latent variables, with various sorts of relationships among the three types of random variables, as ...
The solution to the brachistochrone problem is the cycloid. An example of an application of the Beltrami identity is the brachistochrone problem , which involves finding the curve y = y ( x ) {\displaystyle y=y(x)} that minimizes the integral