A number of new generally covariant identities which involve second derivatives of the Riemann tensor are presented. Each of these new identities can be expressed by equating to zero either (a) a particular sum of terms each of which contains an operator of the form (▿μ▿ν − ▿ν▿μ) acting on the Riemann tensor; or (b) a particular sum of terms each of which contains an operator of the form ▿a acting either on the expression or on the expression ; or (c) a particular sum of algebraic terms each of which contains no derivatives of the Riemann tensor, but rather is quadratic in the Riemann tensor. Each of the new identities can be expressed in all three of the above‐described forms. Furthermore, each of these new identities can be thought of as an integrability condition derived from the equations that define the Riemann tensor in terms of the or the gμν. The requirements of Riquier's existence theorem are used to guide the derivation of the identities. The operator ▿μ denotes covariant differentiation. All the new identities assume the existence of a symmetric connection and one of the new identities assumes the existence of a metric. Schouten's identity and Walker's identity are also discussed.
Skip Nav Destination
Article navigation
March 1974
Research Article|
March 01 1974
New commutator identities on the Riemann tensor
C. Martin Pereira
C. Martin Pereira
Center for Theoretical Physics and Department of Physics & Astronomy, University of Maryland, College Park, Maryland 20742
Search for other works by this author on:
J. Math. Phys. 15, 269–272 (1974)
Article history
Received:
August 08 1973
Citation
C. Martin Pereira; New commutator identities on the Riemann tensor. J. Math. Phys. 1 March 1974; 15 (3): 269–272. https://doi.org/10.1063/1.1666635
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
Graded Poisson and graded Dirac structures
Manuel de León, Rubén Izquierdo-López
Mathematical models of human memory
Mikhail Katkov, Michelangelo Naim, et al.
Connecting stochastic optimal control and reinforcement learning
J. Quer, Enric Ribera Borrell