*******************************************************************	INVERT Version 45G********************************************************************  Invert a covariance matrix using Choleski decomposition method**  Inputs:*    ORDER            - Analysis order*    PHI(ORDER,ORDER) - Covariance matrix*    PSI(ORDER)       - Column vector to be predicted*  Outputs:*    RC(ORDER)        - Pseudo reflection coefficients*  Internal:*    V(ORDER,ORDER)   - Temporary matrix*    X(ORDER)         - Column scaling factors**  NOTE: Temporary matrix V is not needed and may be replaced*    by PHI if the original PHI values do not need to be preserved.*	SUBROUTINE INVERT( ORDER, PHI, PSI, RC )	INCLUDE 'config.fh'	INTEGER ORDER, I, J, K	REAL PHI(ORDER,ORDER), PSI(ORDER), RC(ORDER)	REAL V(MAXORD,MAXORD), SAVE, EPS	PARAMETER (EPS=1.0E-10)*  Decompose PHI into V * D * V' where V is a triangular matrix whose*   main diagonal elements are all 1, V' is the transpose of V, and*   D is a vector.  Here D(n) is stored in location V(n,n).	DO J = 1,ORDER	   DO I = J,ORDER	      V(I,J) = PHI(I,J)	   END DO	   DO K = 1,J-1	      SAVE = V(J,K)*V(K,K)	      DO I = J,ORDER	         V(I,J) = V(I,J) - V(I,K)*SAVE	      END DO	   END DO*  Compute intermediate results, which are similar to RC's	   IF (ABS(V(J,J)) .LT. EPS) GOTO 100	   RC(J) = PSI(J)	   DO K = 1,J-1	      RC(J) = RC(J) - RC(K)*V(J,K)	   END DO	   V(J,J) = 1./V(J,J)	   RC(J) = RC(J)*V(J,J)	   RC(J) = MAX(MIN(RC(J),.999),-.999)	END DO	RETURN*  Zero out higher order RC's if algorithm terminated early100	DO I = J,ORDER	   RC(I) = 0.	END DO*  Back substitute for PC's (if needed)*110	DO J = ORDER,1,-1*	   PC(J) = RC(J)*	   DO I = 1,J-1*	      PC(J) = PC(J) - PC(I)*V(J,I)*	   END DO*	END DO	RETURN	END