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java.lang.Object optimization.Uncmin_f77
public class Uncmin_f77
This class contains Java translations of the UNCMIN unconstrained optimization routines. See R.B. Schnabel, J.E. Koontz, and B.E. Weiss, A Modular System of Algorithms for Unconstrained Minimization, Report CUCS24082, Comp. Sci. Dept., University of Colorado at Boulder, 1982.
IMPORTANT: The "_f77" suffixes indicate that these routines use FORTRAN style indexing. For example, you will see
for (i = 1; i <= n; i++)rather than
for (i = 0; i < n; i++)To use the "_f77" routines you will have to declare your vectors and matrices to be one element larger (e.g., v[101] rather than v[100], and a[101][101] rather than a[100][100]), and you will have to fill elements 1 through n rather than elements 0 through n  1. Versions of these programs that use C/Java style indexing will eventually be available. They will end with the suffix "_j".
This class was translated by a statistician from a FORTRAN version of UNCMIN. It is NOT an official translation. It wastes memory by failing to use the first elements of vectors. When public domain Java optimization routines become available from the people who produced UNCMIN, then THE CODE PRODUCED BY THE NUMERICAL ANALYSTS SHOULD BE USED.
Meanwhile, if you have suggestions for improving this code, please contact Steve Verrill at steve@ws10.fpl.fs.fed.us.
Constructor Summary  

Uncmin_f77()

Method Summary  

static void 
bakslv_f77(int n,
double[][] a,
double[] x,
double[] b)
The bakslv_f77 method solves Ax = b where A is an upper triangular matrix. 
static void 
chlhsn_f77(int n,
double[][] a,
double epsm,
double[] sx,
double[] udiag)
The chlhsn_f77 method finds "THE L(LTRANSPOSE) [WRITTEN LL+] DECOMPOSITION OF THE PERTURBED MODEL HESSIAN MATRIX A+MU*I(WHERE MU\0 AND I IS THE IDENTITY MATRIX) WHICH IS SAFELY POSITIVE DEFINITE. 
static void 
choldc_f77(int n,
double[][] a,
double diagmx,
double tol,
double[] addmax)
The choldc_f77 method finds "THE PERTURBED L(LTRANSPOSE) [WRITTEN LL+] DECOMPOSITION OF A+D, WHERE D IS A NONNEGATIVE DIAGONAL MATRIX ADDED TO A IF NECESSARY TO ALLOW THE CHOLESKY DECOMPOSITION TO CONTINUE." Translated by Steve Verrill, April 15, 1998. 
static void 
dfault_f77(int n,
double[] x,
double[] typsiz,
double[] fscale,
int[] method,
int[] iexp,
int[] msg,
int[] ndigit,
int[] itnlim,
int[] iagflg,
int[] iahflg,
double[] dlt,
double[] gradtl,
double[] stepmx,
double[] steptl)
The dfault_f77 method sets default values for each input variable to the minimization algorithm. 
static void 
dogdrv_f77(int n,
double[] x,
double[] f,
double[] g,
double[][] a,
double[] p,
double[] xpls,
double[] fpls,
optimization.Uncmin_methods minclass,
double[] sx,
double[] stepmx,
double[] steptl,
double[] dlt,
int[] iretcd,
boolean[] mxtake,
double[] sc,
double[] wrk1,
double[] wrk2,
double[] wrk3)
The dogdrv_f77 method finds the next Newton iterate (xpls) by the double dogleg method. 
static void 
dogstp_f77(int n,
double[] g,
double[][] a,
double[] p,
double[] sx,
double rnwtln,
double[] dlt,
boolean[] nwtake,
boolean[] fstdog,
double[] ssd,
double[] v,
double[] cln,
double[] eta,
double[] sc,
double[] stepmx)
The dogstp_f77 method finds the new step by the double dogleg appproach. 
static void 
forslv_f77(int n,
double[][] a,
double[] x,
double[] b)
The forslv_f77 method solves Ax = b where A is a lower triangular matrix. 
static void 
fstocd_f77(int n,
double[] x,
optimization.Uncmin_methods minclass,
double[] sx,
double rnoise,
double[] g)
The fstocd_f77 method finds a central difference approximation to the gradient of the function to be minimized. 
static void 
fstofd_f77(int n,
double[] xpls,
optimization.Uncmin_methods minclass,
double[] fpls,
double[][] a,
double[] sx,
double rnoise,
double[] fhat)
This version of the fstofd_f77 method finds a finite difference approximation to the Hessian. 
static void 
fstofd_f77(int n,
double[] xpls,
optimization.Uncmin_methods minclass,
double[] fpls,
double[] g,
double[] sx,
double rnoise)
This version of the fstofd_f77 method finds first order finite difference approximations for gradients. 
static void 
grdchk_f77(int n,
double[] x,
optimization.Uncmin_methods minclass,
double[] f,
double[] g,
double[] typsiz,
double[] sx,
double[] fscale,
double rnf,
double analtl,
double[] gest)
The grdchk_f77 method checks the analytic gradient supplied by the user. 
static void 
heschk_f77(int n,
double[] x,
optimization.Uncmin_methods minclass,
double[] f,
double[] g,
double[][] a,
double[] typsiz,
double[] sx,
double rnf,
double analtl,
int[] iagflg,
double[] udiag,
double[] wrk1,
double[] wrk2)
The heschk_f77 method checks the analytic Hessian supplied by the user. 
static void 
hookdr_f77(int n,
double[] x,
double[] f,
double[] g,
double[][] a,
double[] udiag,
double[] p,
double[] xpls,
double[] fpls,
optimization.Uncmin_methods minclass,
double[] sx,
double[] stepmx,
double[] steptl,
double[] dlt,
int[] iretcd,
boolean[] mxtake,
double[] amu,
double[] dltp,
double[] phi,
double[] phip0,
double[] sc,
double[] xplsp,
double[] wrk0,
double epsm,
int[] itncnt)
The hookdr_f77 method finds a next Newton iterate (xpls) by the MoreHebdon technique. 
static void 
hookst_f77(int n,
double[] g,
double[][] a,
double[] udiag,
double[] p,
double[] sx,
double rnwtln,
double[] dlt,
double[] amu,
double[] dltp,
double[] phi,
double[] phip0,
boolean[] fstime,
double[] sc,
boolean[] nwtake,
double[] wrk0,
double epsm)
The hookst_f77 method finds a new step by the MoreHebdon algorithm. 
static void 
hsnint_f77(int n,
double[][] a,
double[] sx,
int[] method)
The hsnint_f77 method provides the initial Hessian when secant updates are being used. 
static void 
lltslv_f77(int n,
double[][] a,
double[] x,
double[] b)
The lltslv_f77 method solves Ax = b where A has the form L(L transpose) but only the lower triangular part, L, is stored. 
static void 
lnsrch_f77(int n,
double[] x,
double[] f,
double[] g,
double[] p,
double[] xpls,
double[] fpls,
optimization.Uncmin_methods minclass,
boolean[] mxtake,
int[] iretcd,
double[] stepmx,
double[] steptl,
double[] sx)
The lnsrch_f77 method finds a next Newton iterate by line search. 
static void 
mvmltl_f77(int n,
double[][] a,
double[] x,
double[] y)
The mvmltl_f77 method computes y = Lx where L is a lower triangular matrix stored in A. 
static void 
mvmlts_f77(int n,
double[][] a,
double[] x,
double[] y)
The mvmlts_f77 method computes y = Ax where A is a symmetric matrix stored in its lower triangular part. 
static void 
mvmltu_f77(int n,
double[][] a,
double[] x,
double[] y)
The mvmltu_f77 method computes Y = (L transpose)X where L is a lower triangular matrix stored in A (L transpose is taken implicitly). 
static void 
optchk_f77(int n,
double[] x,
double[] typsiz,
double[] sx,
double[] fscale,
double[] gradtl,
int[] itnlim,
int[] ndigit,
double epsm,
double[] dlt,
int[] method,
int[] iexp,
int[] iagflg,
int[] iahflg,
double[] stepmx,
int[] msg)
The optchk_f77 method checks the input for reasonableness. 
static void 
optdrv_f77(int n,
double[] x,
optimization.Uncmin_methods minclass,
double[] typsiz,
double[] fscale,
int[] method,
int[] iexp,
int[] msg,
int[] ndigit,
int[] itnlim,
int[] iagflg,
int[] iahflg,
double[] dlt,
double[] gradtl,
double[] stepmx,
double[] steptl,
double[] xpls,
double[] fpls,
double[] gpls,
int[] itrmcd,
double[][] a,
double[] udiag,
double[] g,
double[] p,
double[] sx,
double[] wrk0,
double[] wrk1,
double[] wrk2,
double[] wrk3)
The optdrv_f77 method is the driver for the nonlinear optimization problem. 
static void 
optif0_f77(int n,
double[] x,
optimization.Uncmin_methods minclass,
double[] xpls,
double[] fpls,
double[] gpls,
int[] itrmcd,
double[][] a,
double[] udiag)
The optif0_f77 method minimizes a smooth nonlinear function of n variables. 
static void 
optif9_f77(int n,
double[] x,
optimization.Uncmin_methods minclass,
double[] typsiz,
double[] fscale,
int[] method,
int[] iexp,
int[] msg,
int[] ndigit,
int[] itnlim,
int[] iagflg,
int[] iahflg,
double[] dlt,
double[] gradtl,
double[] stepmx,
double[] steptl,
double[] xpls,
double[] fpls,
double[] gpls,
int[] itrmcd,
double[][] a,
double[] udiag)
The optif9_f77 method minimizes a smooth nonlinear function of n variables. 
static void 
optstp_f77(int n,
double[] xpls,
double[] fpls,
double[] gpls,
double[] x,
int[] itncnt,
int[] icscmx,
int[] itrmcd,
double[] gradtl,
double[] steptl,
double[] sx,
double[] fscale,
int[] itnlim,
int[] iretcd,
boolean[] mxtake,
int[] msg)
The optstp_f77 method determines whether the algorithm should terminate due to any of the following: 1) problem solved within user tolerance 2) convergence within user tolerance 3) iteration limit reached 4) divergence or too restrictive maximum step (stepmx) suspected Translated by Steve Verrill, May 12, 1998. 
static void 
qraux1_f77(int n,
double[][] r,
int i)
The qraux1_f77 method interchanges rows i,i+1 of the upper Hessenberg matrix r, columns i to n. 
static void 
qraux2_f77(int n,
double[][] r,
int i,
double a,
double b)
The qraux2_f77 method premultiplies r by the Jacobi rotation j(i,i+1,a,b). 
static void 
qrupdt_f77(int n,
double[][] a,
double[] u,
double[] v)
The qrupdt_f77 method finds an orthogonal n by n matrix, Q*, and an upper triangular n by n matrix, R*, such that (Q*)(R*) = R+U(V+). 
static void 
result_f77(int n,
double[] x,
double[] f,
double[] g,
double[][] a,
double[] p,
int[] itncnt,
int iflg)
The result_f77 method prints information. 
static void 
sclmul_f77(int n,
double s,
double[] v,
double[] z)
The sclmul_f77 method multiplies a vector by a scalar. 
static void 
secfac_f77(int n,
double[] x,
double[] g,
double[][] a,
double[] xpls,
double[] gpls,
double epsm,
int[] itncnt,
double rnf,
int[] iagflg,
boolean[] noupdt,
double[] s,
double[] y,
double[] u,
double[] w)
The secfac_f77 method updates the Hessian by the BFGS factored technique. 
static void 
secunf_f77(int n,
double[] x,
double[] g,
double[][] a,
double[] udiag,
double[] xpls,
double[] gpls,
double epsm,
int[] itncnt,
double rnf,
int[] iagflg,
boolean[] noupdt,
double[] s,
double[] y,
double[] t)
The secunf_f77 method updates the Hessian by the BFGS unfactored approach. 
static void 
sndofd_f77(int n,
double[] xpls,
optimization.Uncmin_methods minclass,
double[] fpls,
double[][] a,
double[] sx,
double rnoise,
double[] stepsz,
double[] anbr)
The sndofd_f77 method finds second order forward finite difference approximations to the Hessian. 
static void 
tregup_f77(int n,
double[] x,
double[] f,
double[] g,
double[][] a,
optimization.Uncmin_methods minclass,
double[] sc,
double[] sx,
boolean[] nwtake,
double[] stepmx,
double[] steptl,
double[] dlt,
int[] iretcd,
double[] xplsp,
double[] fplsp,
double[] xpls,
double[] fpls,
boolean[] mxtake,
int method,
double[] udiag)
The tregup_f77 method decides whether to accept xpls = x + sc as the next iterate and update the trust region dlt. 
Methods inherited from class java.lang.Object 

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait 
Constructor Detail 

public Uncmin_f77()
Method Detail 

public static void optif0_f77(int n, double[] x, optimization.Uncmin_methods minclass, double[] xpls, double[] fpls, double[] gpls, int[] itrmcd, double[][] a, double[] udiag)
The optif0_f77 method minimizes a smooth nonlinear function of n variables. A method that computes the function value at any point must be supplied. (See Uncmin_methods.java and UncminTest.java.) Derivative values are not required. The optif0_f77 method provides the simplest user access to the UNCMIN minimization routines. Without a recompile, the user has no control over options. For details, see the Schnabel et al reference and the comments in the code. Translated by Steve Verrill, August 4, 1998.
n
 The number of arguments of the function to minimizex
 The initial estimate of the minimum pointminclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.xpls
 The final estimate of the minimum pointfpls
 The value of f_to_minimize at xplsgpls
 The gradient at the local minimum xplsitrmcd
 Termination code ITRMCD = 0: Optimal solution found ITRMCD =
1: Terminated with gradient small, xpls is probably optimal
ITRMCD = 2: Terminated with stepsize small, xpls is probably
optimal ITRMCD = 3: Lower point cannot be found, xpls is
probably optimal ITRMCD = 4: Iteration limit (150) exceeded
ITRMCD = 5: Too many large steps, function may be unboundeda
 Workspace for the Hessian (or its estimate) and its Cholesky
decompositionudiag
 Workspace for the diagonal of the Hessianpublic static void optif9_f77(int n, double[] x, optimization.Uncmin_methods minclass, double[] typsiz, double[] fscale, int[] method, int[] iexp, int[] msg, int[] ndigit, int[] itnlim, int[] iagflg, int[] iahflg, double[] dlt, double[] gradtl, double[] stepmx, double[] steptl, double[] xpls, double[] fpls, double[] gpls, int[] itrmcd, double[][] a, double[] udiag)
The optif9_f77 method minimizes a smooth nonlinear function of n variables. A method that computes the function value at any point must be supplied. (See Uncmin_methods.java and UncminTest.java.) Derivative values are not required. The optif9 method provides complete user access to the UNCMIN minimization routines. The user has full control over options. For details, see the Schnabel et al reference and the comments in the code. Translated by Steve Verrill, August 4, 1998.
n
 The number of arguments of the function to minimizex
 The initial estimate of the minimum pointminclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.typsiz
 Typical size for each component of xfscale
 Estimate of the scale of the objective functionmethod
 Algorithm to use to solve the minimization problem = 1 line
search = 2 double dogleg = 3 MoreHebdoniexp
 = 1 if the optimization function f_to_minimize is expensive to
evaluate, = 0 otherwise. If iexp = 1, then the Hessian will be
evaluated by secant update rather than analytically or by
finite differences.msg
 Message to inhibit certain automatic checks and outputndigit
 Number of good digits in the minimization functionitnlim
 Maximum number of allowable iterationsiagflg
 = 0 if an analytic gradient is not suppliediahflg
 = 0 if an analytic Hessian is not supplieddlt
 Trust region radiusgradtl
 Tolerance at which the gradient is considered close enough to
zero to terminate the algorithmstepmx
 Maximum allowable step sizesteptl
 Relative step size at which successive iterates are considered
close enough to terminate the algorithmxpls
 The final estimate of the minimum pointfpls
 The value of f_to_minimize at xplsgpls
 The gradient at the local minimum xplsitrmcd
 Termination code ITRMCD = 0: Optimal solution found ITRMCD =
1: Terminated with gradient small, X is probably optimal
ITRMCD = 2: Terminated with stepsize small, X is probably
optimal ITRMCD = 3: Lower point cannot be found, X is probably
optimal ITRMCD = 4: Iteration limit (150) exceeded ITRMCD = 5:
Too many large steps, function may be unboundeda
 Workspace for the Hessian (or its estimate) and its Cholesky
decompositionudiag
 Workspace for the diagonal of the Hessianpublic static void bakslv_f77(int n, double[][] a, double[] x, double[] b)
The bakslv_f77 method solves Ax = b where A is an upper triangular matrix. Note that A is input as a lower triangular matrix and this method takes its transpose implicitly. Translated by Steve Verrill, April 14, 1998.
n
 Dimension of the problema
 n by n lower triangular matrix (preserved)x
 The solution vectorb
 The righthand side vectorpublic static void chlhsn_f77(int n, double[][] a, double epsm, double[] sx, double[] udiag)
The chlhsn_f77 method finds "THE L(LTRANSPOSE) [WRITTEN LL+] DECOMPOSITION OF THE PERTURBED MODEL HESSIAN MATRIX A+MU*I(WHERE MU\0 AND I IS THE IDENTITY MATRIX) WHICH IS SAFELY POSITIVE DEFINITE. IF A IS SAFELY POSITIVE DEFINITE UPON ENTRY, THEN MU=0." Translated by Steve Verrill, April 14, 1998.
n
 Dimension of the problema
 On entry: A is the model Hessian (only the lower triangle and
diagonal stored) On exit: A contains L of the LL+
decomposition of the perturbed model Hessian in the lower
triangle and diagonal, and contains the Hessian in the upper
triangle and udiagepsm
 Machine epsilonsx
 Scaling vector for xudiag
 On exit: Contains the diagonal of the Hessianpublic static void choldc_f77(int n, double[][] a, double diagmx, double tol, double[] addmax)
The choldc_f77 method finds "THE PERTURBED L(LTRANSPOSE) [WRITTEN LL+] DECOMPOSITION OF A+D, WHERE D IS A NONNEGATIVE DIAGONAL MATRIX ADDED TO A IF NECESSARY TO ALLOW THE CHOLESKY DECOMPOSITION TO CONTINUE." Translated by Steve Verrill, April 15, 1998.
n
 Dimension of the problema
 On entry: matrix for which to find the perturbed Cholesky
decomposition On exit: contains L of the LL+ decomposition in
lower trianglediagmx
 Maximum diagonal element of "A"tol
 Toleranceaddmax
 Maximum amount implicitly added to diagonal of "A" in forming
the Cholesky decomposition of A+Dpublic static void dfault_f77(int n, double[] x, double[] typsiz, double[] fscale, int[] method, int[] iexp, int[] msg, int[] ndigit, int[] itnlim, int[] iagflg, int[] iahflg, double[] dlt, double[] gradtl, double[] stepmx, double[] steptl)
The dfault_f77 method sets default values for each input variable to the minimization algorithm. Translated by Steve Verrill, August 4, 1998.
n
 Dimension of the problemx
 Initial estimate of the solution (to compute max step size)typsiz
 Typical size for each component of xfscale
 Estimate of the scale of the minimization functionmethod
 Algorithm to use to solve the minimization problemiexp
 = 0 if the minimization function is not expensive to evaluatemsg
 Message to inhibit certain automatic checks and outputndigit
 Number of good digits in the minimization functionitnlim
 Maximum number of allowable iterationsiagflg
 = 0 if an analytic gradient is not suppliediahflg
 = 0 if an analytic Hessian is not supplieddlt
 Trust region radiusgradtl
 Tolerance at which the gradient is considered close enough to
zero to terminate the algorithmstepmx
 "Value of zero to trip default maximum in optchk"steptl
 Tolerance at which successive iterates are considered close
enough to terminate the algorithmpublic static void dogdrv_f77(int n, double[] x, double[] f, double[] g, double[][] a, double[] p, double[] xpls, double[] fpls, optimization.Uncmin_methods minclass, double[] sx, double[] stepmx, double[] steptl, double[] dlt, int[] iretcd, boolean[] mxtake, double[] sc, double[] wrk1, double[] wrk2, double[] wrk3)
The dogdrv_f77 method finds the next Newton iterate (xpls) by the double dogleg method. It drives dogstp_f77. Translated by Steve Verrill, April 15, 1998.
n
 Dimension of the problemx
 The old iteratef
 Function value at the old iterateg
 Gradient or approximation at the old iteratea
 Cholesky decomposition of Hessian in lower triangular part and
diagonalp
 Newton stepxpls
 The new iteratefpls
 Function value at the new iterateminclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.sx
 Scaling vector for xstepmx
 Maximum allowable step sizesteptl
 Relative step size at which successive iterates are considered
close enough to terminate the algorithmdlt
 Trust region radius (value needs to be retained between
successive calls)iretcd
 Return code: 0  satisfactory xpls found 1  failed to
find satisfactory xpls sufficently distinct from xmxtake
 Boolean flag indicating that a step of maximum length was used
lengthsc
 Workspace (current step)wrk1
 Workspace (and place holding argument to tregup)wrk2
 Workspacewrk3
 Workspacepublic static void dogstp_f77(int n, double[] g, double[][] a, double[] p, double[] sx, double rnwtln, double[] dlt, boolean[] nwtake, boolean[] fstdog, double[] ssd, double[] v, double[] cln, double[] eta, double[] sc, double[] stepmx)
The dogstp_f77 method finds the new step by the double dogleg appproach. Translated by Steve Verrill, April 21, 1998.
n
 DIMENSION OF PROBLEMg
 GRADIENT AT CURRENT ITERATE, G(X)a
 CHOLESKY DECOMPOSITION OF HESSIAN IN LOWER PART AND DIAGONALp
 NEWTON STEPsx
 Scaling vector for xrnwtln
 NEWTON STEP LENGTHdlt
 TRUST REGION RADIUSnwtake
 BOOLEAN, = true IF NEWTON STEP TAKENfstdog
 BOOLEAN, = true IF ON FIRST LEG OF DOGLEGssd
 WORKSPACE [CAUCHY STEP TO THE MINIMUM OF THE QUADRATIC MODEL
IN THE SCALED STEEPEST DESCENT DIRECTION] [RETAIN VALUE
BETWEEN SUCCESSIVE CALLS]v
 WORKSPACE [RETAIN VALUE BETWEEN SUCCESSIVE CALLS]cln
 CAUCHY LENGTH [RETAIN VALUE BETWEEN SUCCESSIVE CALLS]eta
 [RETAIN VALUE BETWEEN SUCCESSIVE CALLS]sc
 CURRENT STEPstepmx
 MAXIMUM ALLOWABLE STEP SIZEpublic static void forslv_f77(int n, double[][] a, double[] x, double[] b)
The forslv_f77 method solves Ax = b where A is a lower triangular matrix. Translated by Steve Verrill, April 21, 1998.
n
 The dimension of the problema
 The lower triangular matrix (preserved)x
 The solution vectorb
 The righthand side vectorpublic static void fstocd_f77(int n, double[] x, optimization.Uncmin_methods minclass, double[] sx, double rnoise, double[] g)
The fstocd_f77 method finds a central difference approximation to the gradient of the function to be minimized. Translated by Steve Verrill, April 21, 1998.
n
 The dimension of the problemx
 The point at which the gradient is to be approximatedminclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.sx
 Scaling vector for xrnoise
 Relative noise in the function to be minimizedg
 A central difference approximation to the gradientpublic static void fstofd_f77(int n, double[] xpls, optimization.Uncmin_methods minclass, double[] fpls, double[][] a, double[] sx, double rnoise, double[] fhat)
This version of the fstofd_f77 method finds a finite difference approximation to the Hessian. Translated by Steve Verrill, April 22, 1998.
n
 The dimension of the problemxpls
 New iterateminclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.fpls
 fpls[1]  fpls[n] contains the gradient of the function to
minimizea
 "FINITE DIFFERENCE APPROXIMATION. ONLY LOWER TRIANGULAR MATRIX
AND DIAGONAL ARE RETURNED"sx
 Scaling vector for xrnoise
 Relative noise in the function to be minimizedfhat
 Workspacepublic static void fstofd_f77(int n, double[] xpls, optimization.Uncmin_methods minclass, double[] fpls, double[] g, double[] sx, double rnoise)
This version of the fstofd_f77 method finds first order finite difference approximations for gradients. Translated by Steve Verrill, April 22, 1998.
n
 The dimension of the problemxpls
 New iterateminclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.fpls
 fpls contains the value of the function to minimize at the new
iterateg
 finite difference approximation to the gradientsx
 Scaling vector for xrnoise
 Relative noise in the function to be minimizedpublic static void grdchk_f77(int n, double[] x, optimization.Uncmin_methods minclass, double[] f, double[] g, double[] typsiz, double[] sx, double[] fscale, double rnf, double analtl, double[] gest)
The grdchk_f77 method checks the analytic gradient supplied by the user. Translated by Steve Verrill, April 22, 1998.
n
 The dimension of the problemx
 The location at which the gradient is to be checkedminclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.f
 Function valueg
 Analytic gradienttypsiz
 Typical size for each component of xsx
 Scaling vector for x: sx[i] = 1.0/typsiz[i]fscale
 Estimate of scale of f_to_minimizernf
 Relative noise in f_to_minimizeanaltl
 Tolerance for comparison of estimated and analytical gradientsgest
 Finite difference gradientpublic static void heschk_f77(int n, double[] x, optimization.Uncmin_methods minclass, double[] f, double[] g, double[][] a, double[] typsiz, double[] sx, double rnf, double analtl, int[] iagflg, double[] udiag, double[] wrk1, double[] wrk2)
The heschk_f77 method checks the analytic Hessian supplied by the user. Translated by Steve Verrill, April 23, 1998.
n
 The dimension of the problemx
 The location at which the Hessian is to be checkedminclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.f
 Function valueg
 Gradienta
 On exit: Hessian in lower triangletypsiz
 Typical size for each component of xsx
 Scaling vector for x: sx[i] = 1.0/typsiz[i]rnf
 Relative noise in f_to_minimizeanaltl
 Tolerance for comparison of estimated and analytic gradientsiagflg
 = 1 if an analytic gradient is suppliedudiag
 Workspacewrk1
 Workspacewrk2
 Workspacepublic static void hookdr_f77(int n, double[] x, double[] f, double[] g, double[][] a, double[] udiag, double[] p, double[] xpls, double[] fpls, optimization.Uncmin_methods minclass, double[] sx, double[] stepmx, double[] steptl, double[] dlt, int[] iretcd, boolean[] mxtake, double[] amu, double[] dltp, double[] phi, double[] phip0, double[] sc, double[] xplsp, double[] wrk0, double epsm, int[] itncnt)
The hookdr_f77 method finds a next Newton iterate (xpls) by the MoreHebdon technique. It drives hookst_f77. Translated by Steve Verrill, April 23, 1998.
n
 The dimension of the problemx
 The old iteratef
 The function value at the old iterateg
 Gradient or approximation at old iteratea
 Cholesky decomposition of Hessian in lower triangle and
diagonal. Hessian in upper triangle and udiag.udiag
 Diagonal of Hessian in ap
 Newton stepxpls
 New iteratefpls
 Function value at the new iterateminclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.sx
 Scaling vector for xstepmx
 Maximum allowable step sizesteptl
 Relative step size at which consecutive iterates are
considered close enough to terminate the algorithmdlt
 Trust region radiusiretcd
 Return code = 0 satisfactory xpls found = 1 failed to find
satisfactory xpls sufficiently distinct from xmxtake
 Boolean flag indicating step of maximum length usedamu
 [Retain value between successive calls]dltp
 [Retain value between successive calls]phi
 [Retain value between successive calls]phip0
 [Retain value between successive calls]sc
 Workspacexplsp
 Workspacewrk0
 Workspaceepsm
 Machine epsilonitncnt
 Iteration countpublic static void hookst_f77(int n, double[] g, double[][] a, double[] udiag, double[] p, double[] sx, double rnwtln, double[] dlt, double[] amu, double[] dltp, double[] phi, double[] phip0, boolean[] fstime, double[] sc, boolean[] nwtake, double[] wrk0, double epsm)
The hookst_f77 method finds a new step by the MoreHebdon algorithm. It is driven by hookdr_f77. Translated by Steve Verrill, April 24, 1998.
n
 The dimension of the problemg
 The gradient at the current iteratea
 Cholesky decomposition of the Hessian in the lower triangle
and diagonal. Hessian or approximation in upper triangle (and
udiag).udiag
 Diagonal of Hessian in ap
 Newton stepsx
 Scaling vector for xrnwtln
 Newton step lengthdlt
 Trust region radiusamu
 Retain value between successive callsdltp
 Trust region radius at last exit from this routinephi
 Retain value between successive callsphip0
 Retain value between successive callsfstime
 "= true if first entry to this routine during the kth
iteration"sc
 Current stepnwtake
 = true if Newton step takenwrk0
 Workspaceepsm
 Machine epsilonpublic static void hsnint_f77(int n, double[][] a, double[] sx, int[] method)
The hsnint_f77 method provides the initial Hessian when secant updates are being used. Translated by Steve Verrill, April 27, 1998.
n
 The dimension of the problema
 Initial Hessian (lower triangular matrix)sx
 Scaling vector for xmethod
 Algorithm to use to solve the minimization problem 1,2 
factored secant method 3  unfactored secant methodpublic static void lltslv_f77(int n, double[][] a, double[] x, double[] b)
The lltslv_f77 method solves Ax = b where A has the form L(L transpose) but only the lower triangular part, L, is stored. Translated by Steve Verrill, April 27, 1998.
n
 The dimension of the problema
 Matrix of form L(L transpose). On return a is unchanged.x
 The solution vectorb
 The righthand side vectorpublic static void lnsrch_f77(int n, double[] x, double[] f, double[] g, double[] p, double[] xpls, double[] fpls, optimization.Uncmin_methods minclass, boolean[] mxtake, int[] iretcd, double[] stepmx, double[] steptl, double[] sx)
The lnsrch_f77 method finds a next Newton iterate by line search. Translated by Steve Verrill, May 15, 1998.
n
 The dimension of the problemx
 Old iteratef
 Function value at old iterateg
 Gradient or approximation at old iteratep
 Nonzero Newton stepxpls
 New iteratefpls
 Function value at new iterateminclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.mxtake
 Boolean flag indicating whether the step of maximum length was
usediretcd
 Return codestepmx
 Maximum allowable step sizesteptl
 Relative step size at which successive iterates are considered
close enough to terminate the algorithmsx
 Scaling vector for xpublic static void mvmltl_f77(int n, double[][] a, double[] x, double[] y)
The mvmltl_f77 method computes y = Lx where L is a lower triangular matrix stored in A. Translated by Steve Verrill, April 27, 1998.
n
 The dimension of the problema
 Lower triangular matrixx
 Operand vectory
 Result vectorpublic static void mvmlts_f77(int n, double[][] a, double[] x, double[] y)
The mvmlts_f77 method computes y = Ax where A is a symmetric matrix stored in its lower triangular part. Translated by Steve Verrill, April 27, 1998.
n
 The dimension of the problema
 The symmetric matrixx
 Operand vectory
 Result vectorpublic static void mvmltu_f77(int n, double[][] a, double[] x, double[] y)
The mvmltu_f77 method computes Y = (L transpose)X where L is a lower triangular matrix stored in A (L transpose is taken implicitly). Translated by Steve Verrill, April 27, 1998.
n
 The dimension of the problema
 The lower triangular matrixx
 Operand vectory
 Result vectorpublic static void optchk_f77(int n, double[] x, double[] typsiz, double[] sx, double[] fscale, double[] gradtl, int[] itnlim, int[] ndigit, double epsm, double[] dlt, int[] method, int[] iexp, int[] iagflg, int[] iahflg, double[] stepmx, int[] msg)
The optchk_f77 method checks the input for reasonableness. Translated by Steve Verrill, May 12, 1998.
n
 The dimension of the problemx
 On entry, estimate of the root of f_to_minimizetypsiz
 Typical size of each component of xsx
 Scaling vector for xfscale
 Estimate of scale of objective functiongradtl
 Tolerance at which the gradient is considered close enough to
zero to terminate the algorithmitnlim
 Maximum number of allowable iterationsndigit
 Number of good digits in the optimization functionepsm
 Machine epsilondlt
 Trust region radiusmethod
 Algorithm indicatoriexp
 Expense flagiagflg
 = 1 if an analytic gradient is suppliediahflg
 = 1 if an analytic Hessian is suppliedstepmx
 Maximum step sizemsg
 Message and error codepublic static void optdrv_f77(int n, double[] x, optimization.Uncmin_methods minclass, double[] typsiz, double[] fscale, int[] method, int[] iexp, int[] msg, int[] ndigit, int[] itnlim, int[] iagflg, int[] iahflg, double[] dlt, double[] gradtl, double[] stepmx, double[] steptl, double[] xpls, double[] fpls, double[] gpls, int[] itrmcd, double[][] a, double[] udiag, double[] g, double[] p, double[] sx, double[] wrk0, double[] wrk1, double[] wrk2, double[] wrk3)
The optdrv_f77 method is the driver for the nonlinear optimization problem. Translated by Steve Verrill, May 18, 1998.
n
 The dimension of the problemx
 On entry, estimate of the location of a minimum of
f_to_minimizeminclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.typsiz
 Typical size of each component of xfscale
 Estimate of scale of objective functionmethod
 Algorithm indicator 1  line search 2  double dogleg 3 
MoreHebdoniexp
 Expense flag. 1  optimization function, f_to_minimize, is
expensive to evaluate 0  otherwise If iexp = 1, the Hessian
will be evaluated by secant update rather than analytically or
by finite differences.msg
 On input: (> 0) message to inhibit certain automatic checks On
output: (< 0) error code (= 0, no error)ndigit
 Number of good digits in the optimization functionitnlim
 Maximum number of allowable iterationsiagflg
 = 1 if an analytic gradient is suppliediahflg
 = 1 if an analytic Hessian is supplieddlt
 Trust region radiusgradtl
 Tolerance at which the gradient is considered close enough to
zero to terminate the algorithmstepmx
 Maximum step sizesteptl
 Relative step size at which successive iterates are considered
close enough to terminate the algorithmxpls
 On exit: xpls is a local minimumfpls
 On exit: function value at xplsgpls
 On exit: gradient at xplsitrmcd
 Termination codea
 workspace for Hessian (or its approximation) and its Cholesky
decompositionudiag
 workspace (for diagonal of Hessian)g
 workspace (for gradient at current iterate)p
 workspace for stepsx
 workspace (for scaling vector)wrk0
 workspacewrk1
 workspacewrk2
 workspacewrk3
 workspacepublic static void optstp_f77(int n, double[] xpls, double[] fpls, double[] gpls, double[] x, int[] itncnt, int[] icscmx, int[] itrmcd, double[] gradtl, double[] steptl, double[] sx, double[] fscale, int[] itnlim, int[] iretcd, boolean[] mxtake, int[] msg)
The optstp_f77 method determines whether the algorithm should terminate due to any of the following: 1) problem solved within user tolerance 2) convergence within user tolerance 3) iteration limit reached 4) divergence or too restrictive maximum step (stepmx) suspected Translated by Steve Verrill, May 12, 1998.
n
 The dimension of the problemxpls
 New iteratefpls
 Function value at new iterategpls
 Gradient or approximation at new iteratex
 Old iterateitncnt
 Current iterationicscmx
 Number of consecutive steps >= stepmx (retain between
successive calls)itrmcd
 Termination codegradtl
 Tolerance at which the relative gradient is considered close
enough to zero to terminate the algorithmsteptl
 Relative step size at which successive iterates are considered
close enough to terminate the algorithmsx
 Scaling vector for xfscale
 Estimate of the scale of the objective functionitnlim
 Maximum number of allowable iterationsiretcd
 Return codemxtake
 Boolean flag indicating step of maximum length was usedmsg
 If msg includes a term 8, suppress outputpublic static void qraux1_f77(int n, double[][] r, int i)
The qraux1_f77 method interchanges rows i,i+1 of the upper Hessenberg matrix r, columns i to n. Translated by Steve Verrill, April 29, 1998.
n
 The dimension of the matrixr
 Upper Hessenberg matrixi
 Index of row to interchange (i < n)public static void qraux2_f77(int n, double[][] r, int i, double a, double b)
The qraux2_f77 method premultiplies r by the Jacobi rotation j(i,i+1,a,b). Translated by Steve Verrill, April 29, 1998.
n
 The dimension of the matrixr
 Upper Hessenberg matrixi
 Index of rowa
 scalarb
 scalarpublic static void qrupdt_f77(int n, double[][] a, double[] u, double[] v)
The qrupdt_f77 method finds an orthogonal n by n matrix, Q*, and an upper triangular n by n matrix, R*, such that (Q*)(R*) = R+U(V+). Translated by Steve Verrill, May 11, 1998.
n
 The dimension of the problema
 On input: contains R On output: contains R*u
 Vectorv
 Vectorpublic static void result_f77(int n, double[] x, double[] f, double[] g, double[][] a, double[] p, int[] itncnt, int iflg)
The result_f77 method prints information. Translated by Steve Verrill, May 11, 1998.
n
 The dimension of the problemx
 Estimate of the location of a minimum at iteration kf
 function value at xg
 gradient at xa
 Hessian at xp
 Step takenitncnt
 Iteration number (k)iflg
 Flag controlling the information to printpublic static void sclmul_f77(int n, double s, double[] v, double[] z)
The sclmul_f77 method multiplies a vector by a scalar. Translated by Steve Verrill, May 8, 1998.
n
 The dimension of the problems
 The scalarv
 Operand vectorz
 Result vectorpublic static void secfac_f77(int n, double[] x, double[] g, double[][] a, double[] xpls, double[] gpls, double epsm, int[] itncnt, double rnf, int[] iagflg, boolean[] noupdt, double[] s, double[] y, double[] u, double[] w)
The secfac_f77 method updates the Hessian by the BFGS factored technique. Translated by Steve Verrill, May 14, 1998.
n
 The dimension of the problemx
 Old iterateg
 Gradient or approximation at the old iteratea
 On entry: Cholesky decomposition of Hessian in lower triangle
and diagonal On exit: Updated Cholesky decomposition of
Hessian in lower triangle and diagonalxpls
 New iterategpls
 Gradient or approximation at the new iterateepsm
 Machine epsilonitncnt
 Iteration countrnf
 Relative noise in optimization function f_to_minimizeiagflg
 1 if an analytic gradient is supplied, 0 otherwisenoupdt
 Boolean: no update yet (retain value between successive calls)s
 Workspacey
 Workspaceu
 Workspacew
 Workspacepublic static void secunf_f77(int n, double[] x, double[] g, double[][] a, double[] udiag, double[] xpls, double[] gpls, double epsm, int[] itncnt, double rnf, int[] iagflg, boolean[] noupdt, double[] s, double[] y, double[] t)
The secunf_f77 method updates the Hessian by the BFGS unfactored approach. Translated by Steve Verrill, May 8, 1998.
n
 The dimension of the problemx
 The old iterateg
 The gradient or an approximation at the old iteratea
 On entry: Approximate Hessian at the old iterate in the upper
triangular part (and udiag) On exit: Updated approximate
Hessian at the new iterate in the lower triangular part and
diagonaludiag
 On entry: Diagonal of Hessianxpls
 New iterategpls
 Gradient or approximation at the new iterateepsm
 Machine epsilonitncnt
 Iteration countrnf
 Relative noise in the optimization function, f_to_minimizeiagflg
 = 1 if an analytic gradient is supplied, = 0 otherwisenoupdt
 Boolean: no update yet (retain value between calls)s
 workspacey
 workspacet
 workspacepublic static void sndofd_f77(int n, double[] xpls, optimization.Uncmin_methods minclass, double[] fpls, double[][] a, double[] sx, double rnoise, double[] stepsz, double[] anbr)
The sndofd_f77 method finds second order forward finite difference approximations to the Hessian. For optimization use this method to estimate the Hessian of the optimization function if no analytical user function has been supplied for either the gradient or the Hessian, and the optimization function is inexpensive to evaluate. Translated by Steve Verrill, May 8, 1998.
n
 The dimension of the problemxpls
 New iterateminclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.fpls
 Function value at the new iteratea
 "FINITE DIFFERENCE APPROXIMATION TO HESSIAN. ONLY LOWER
TRIANGULAR MATRIX AND DIAGONAL ARE RETURNED"sx
 Scaling vector for xrnoise
 Relative noise in the function to be minimizedstepsz
 Workspace (stepsize in ith component direction)anbr
 Workspace (neighbor in ith direction)public static void tregup_f77(int n, double[] x, double[] f, double[] g, double[][] a, optimization.Uncmin_methods minclass, double[] sc, double[] sx, boolean[] nwtake, double[] stepmx, double[] steptl, double[] dlt, int[] iretcd, double[] xplsp, double[] fplsp, double[] xpls, double[] fpls, boolean[] mxtake, int method, double[] udiag)
The tregup_f77 method decides whether to accept xpls = x + sc as the next iterate and update the trust region dlt. Translated by Steve Verrill, May 11, 1998.
n
 The dimension of the problemx
 Old iteratef
 Function value at old iterateg
 Gradient or approximation at old iteratea
 Cholesky decomposition of Hessian in lower triangular part and
diagonal. Hessian or approximation in upper triangular part.minclass
 A class that implements the Uncmin_methods interface (see the
definition in Uncmin_methods.java). See UncminTest_f77.java
for an example of such a class. The class must define: 1.) a
method, f_to_minimize, to minimize. f_to_minimize must have
the form
public static double f_to_minimize(double x[])
where x is the vector of arguments to the function and the
return value is the value of the function evaluated at x. 2.)
a method, gradient, that has the form
public static void gradient(double x[], double g[])
where g is the gradient of f evaluated at x. This method will
have an empty body if the user does not wish to provide an
analytic estimate of the gradient. 3.) a method, hessian, that
has the form
public static void hessian(double x[], double h[][]) where h
is the Hessian of f evaluated at x. This method will have an
empty body if the user does not wish to provide an analytic
estimate of the Hessian. If the user wants Uncmin to check the
Hessian, then the hessian method should only fill the lower
triangle (and diagonal) of h.sc
 Current stepsx
 Scaling vector for xnwtake
 Boolean, = true if Newton step takenstepmx
 Maximum allowable step sizesteptl
 Relative step size at which successive iterates are considered
close enough to terminate the algorithmdlt
 Trust region radiusiretcd
 Return code = 0 xpls accepted as next iterate, dlt is the
trust region radius for the next iteration = 1 xpls
unsatisfactory but accepted as next iterate because xpls  x
is less than the smallest allowable step length = 2 f(xpls)
too large. Continue current iteration with new reduced dlt. =
3 f(xpls) sufficiently small, but quadratic model predicts
f(xpls) sufficiently well to continue current iteration with
new doubled dlt.xplsp
 Workspace (value needs to be retained between successive calls
of kth global step)fplsp
 Retain between successive callsxpls
 New iteratefpls
 Function value at new iteratemxtake
 Boolean flag indicating step of maximum length usedmethod
 Algorithm to use to solve minimization problem = 1 Line search
= 2 Double dogleg = 3 MoreHebdonudiag
 Diagonal of Hessian in a

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