FSQP - SLICOT Library Routine Documentation

## FSQP

### Minimization of the maximum of a set of smooth objective functions

[Specification] [Arguments] [Method] [References] [Comments] [Example]

Purpose

      To perform the minimization of the maximum of a set of smooth
      objective functions (possibly a single one or none at all) subject
      to nonlinear equality and inequality constraints, linear equality
      and inequality constraints, and simple bounds on the variables.
      Specifically, the problem to solve is of the form

                               min max{f (x)}
                                    i   i

                                                n
      where 1 <= i <= NF, and x is a vector in R  subject to the
      following constraints

       bl <= x <= bu

       g (x)<=0,    j = 1 ... NINEQN
        j
       g (x)<=0,    j = NINEQN+1 ... NINEQ
        j
       h (x)=0,     j = 1 ... NEQN
        j
       h (x)=0,     j = NEQN+1 ... NEQ
        j

                                      n
      where bl and bu are vectors in R , and

           n
      f : R -> R,   j = 1 ... NF,           smooth
       j
           n
      g : R -> R,   j = 1 ... NINEQN,       nonlinear and smooth
       j
           n
      g : R -> R,   j = NINEQN+1 ... NINEQ, linear
       j
           n
      h : R -> R,   j = 1 ... NEQN,         nonlinear and smooth
       j
           n
      h : R -> R,   j = NEQN+1 ... NEQ,     linear
       j

Specification

          SUBROUTINE FSQP( MODE, IPRINT, OBJ, CONSTR, GRADOB, GRADCN,
         &                 NPARAM, NF, NINEQN, NINEQ, NEQN, NEQ, MITER,
         &                 UDELTA, BIGBND, BL, BU, X, F, G, TOL1, TOL2,
         &                 IWORK, LIWORK, DWORK, LDWORK, INFO)
    C     .. Scalar Arguments ..
          INTEGER           NPARAM, NF, NINEQN, NINEQ, NEQN, NEQ, MODE,
         &                  IPRINT, MITER, INFO, LIWORK, LDWORK
          DOUBLE PRECISION  BIGBND, TOL1, TOL2, UDELTA
    C     .. Array Arguments ..
          INTEGER           IWORK(LIWORK)
          DOUBLE PRECISION  BL(*), BU(*), X(*), F(*), G(*), DWORK(LDWORK)

Arguments

Mode Parameters

      MODE    (input) INTEGER
              mode = CBA (a 3-digit number) with the following meaning
              (see [1] for more details):

              A specifies the problem to be solved:
              A = 0: The problem is that described above.
              A = 1: In the problem described above, the function to
                     minimize is replaced by
                               min max{ABS(f (x))}
                                    i       i
                     i.e., absolute values of the objective functions
                     are taken.

              B indicates the method to be used:
              B = 0: Algorithm FFSQP-AL is selected (see METHOD below).
              B = 1: Algorithm FFSQP-NL is selected (see METHOD below).

              C indicates the order of evaluation of objective
              functions and constraints during the line search:
              C = 1: The function that caused the previous value of t
                     to be rejected is checked first and all functions
                     of the same type ("objective" or "constraint") as
                     the latter will then be checked first (recommended
                     for most users).
              C = 2: Constraints will be always checked first at each
                     trial point during the line search. If it is a
                     contraint that caused the previous value of t to
                     be rejected, that constraint will be checked first
                     (useful when objective functions are not defined or
                     are difficult to evaluate outside of the feasible
                     region).

      IPRINT  (input) INTEGER
              Parameter indicating the desired output (see [1] for a
              more complete description of the output).
              IPRINT = 0: No information except for user-input errors
                 is displayed. This value is imposed during phase 1.
              IPRINT = 1: Objective and constraint values at the
                 initial feasible point are displayed. At the end of
                 execution, status (INFO), iterate, objective values,
                 constraint values, number of evaluations of objectives
                 and nonlinear constraints, norm of the Kuhn-Tucker
                 vector, and sum of feasibility violation are
                 displayed.
              IPRINT = 2: At the end of each iteration, the same
                 information as with IPRINT = 1 is displayed.
              IPRINT = 3: At each iteration, the same information as
                 with IPRINT = 2,including detailed information on the
                 search direction computation, on the line search, and
                 on the update, is displayed.
              IPRINT = 10*N +M: N any positive integer, M=2 or 3.
                 Information corresponding to IPRINT=M is displayed at
                 every (10*N)th iteration and at the last iteration.

User-supplied Subroutines

      OBJ     SUBROUTINE
              Computes the value of the objective functions. If NF = 0,
              a (dummy) subroutine must be provided anyway. The
              specification of OBJ is

              SUBROUTINE OBJ(NPARAM,J,X,FJ)
              INTEGER NPARAM, J
              DOUBLE PRECISION X(NPARAM),FJ

              Arguments:
              NPARAM (Input) Dimension of X.
              J (Input) Number of the objective to be computed.
              X (Input) Current iterate.
              FJ (Output) Value of the jth objective function at X.

      CONSTR  SUBROUTINE
              Computes the value of the constraints. If there are no
              constraints, a (dummy) subroutine must be provided anyway.
              The specification of CONSTR is as follows.

              SUBROUTINE CONSTR(NPARAM,J,X,GJ)
              INTEGER NPARAM,J
              DOUBLE PRECISION X(NPARAM),GJ

              Arguments:
              NPARAM (Input) Dimension of X.
              J (Input) Number of the constraint to be computed.
              X (Input) Current iterate.
              GJ (Output) Value of the jth constraint at X.

              The order of the constraints must be as follows. First
              the NINEQN (possibly zero) nonlinear inequality
              constraints. Then the NINEQ-NINEQN (possibly zero) linear
              inequality constraints. Finally, the NEQN (possibly zero)
              nonlinear equality constraints followed by the NEQ-NEQN
              (possibly zero) linear equality constraints.

      GRADOB  SUBROUTINE
              Computes the gradients of the objective functions. The
              user must pass the subroutine name GROBFD, if he/she
              wishes that FSQP evaluate these gradients automatically,
              by forward finite differences. The specification of GRADOB
              is as follows.

              SUBROUTINE GRADOB(NPARAM,J,X,GRADFJ,DUMMY)
              INTEGER NPARAM,J
              DOUBLE PRECISION X(NPARAM),GRADFJ(NPARAM)
              DOUBLE PRECISION DUMMY
              EXTERNAL DUMMY

              Arguments:
              NPARAM (Input) Dimension of X.
              J (Input) Number of objective for which gradient is to be
                    computed.
              X (Input) Current iterate.
              GRADFJ (Output) Gradient of the jth objective function at
                    X.
              DUMMY (Input) Used by GROBFD (internally assigned the
                    name of the objective function subroutine by FFSQP).

              Note that DUMMY is passed as argument to GRADOB to allow
              for forward finite difference computation of the gradient.

      GRADCN  SUBROUTINE
              Computes the gradients of the constraints. The
              user must pass the subroutine name GRCNFD, if he/she
              wishes that FSQP evaluate these gradients automatically,
              by forward finite differences. The specification of GRADCN
              is as follows

              SUBROUTINE GRADCN (NPARAM,J,X,GRADGJ,DUMMY)
              INTEGER NPARAM,J
              DOUBLE PRECISION X(NPARAM),GRADGJ(NPARAM)
              DOUBLE PRECISION DUMMY
              EXTERNAL DUMMY

              Arguments:
              NPARAM (Input) Dimension of X.
              J (Input) Number of constraint for which gradient is to
                    be computed.
              X (Input) Current iterate.
              GRADGJ (Output) Gradient of the jth constraint evaluated
                    at X.
              DUMMY (Input) Used by GRCNFD (internally assigned the
                    name of the constraint function subroutine by
                    FFSQP).

              Note that DUMMY is passed as argument to GRADCN to allow
              for forward finite difference computation of the gradients.

Input/Output Parameters

      NPARAM  (input) INTEGER
              Number of free variables, i.e., the dimension of X.

      NF      (input) INTEGER
              Number of objective functions (possibly zero).

      NINEQN  (input) INTEGER
              Number (possibly zero) of nonlinear inequality
              constraints.

      NINEQ   (input) INTEGER
              Total number (possibly equal to nineqn) of inequality
              constraints.

      NEQN    (input) INTEGER
              Number (possibly zero) of nonlinear equality constraints.

      NEQ     (input) INTEGER
              Total number (possibly equal to neqn) of equality
              constraints.

      MITER   (input) INTEGER
              Maximum number of iterations allowed by the user before
              termination of execution.

      UDELTA  (input) DOUBLE PRECISION
              The perturbation size the user suggests to use in
              approximating gradients by finite difference. UDELTA
              should be set to zero if the user has no idea how to
              choose it. See [1] for details.

      BIGBND  (input) DOUBLE PRECISION
              It plays the role of Infinite Bound (see also BL and BU
              below).

      BL      (input) DOUBLE PRECISION array, dimension (NPARAM)
              Lower bounds for the components of X. To specify a non-
              existent lower bound for some j, the value used must
              satisfy BL(j) <= -BIGBND.

      BU      (input) DOUBLE PRECISION array, dimension (NPARAM)
              Upper bounds for the components of X. To specify a non-
              existent upper bound for some j, the value used must
              satisfy BU(j) >= BIGBND.

      X       (input/output) DOUBLE PRECISION array, dimension (NPARAM)
              On entry, this is the initial guess.
              On exit, this is the iterate at the end of execution.

      F       (output) DOUBLE PRECISION array, dimension ( MAX(1,NF) )
              Value of functions f_i, i = 1, ..., NF, at X at the end
              of execution.

      G       (output) DOUBLE PRECISION array, dimension
              ( MAX(1,NINEQ+NEQ) )
              Value of constraint functions at X at the end of
              execution.

Tolerances

      TOL1    DOUBLE PRECISION
              Corresponds to argument EPS in [1].
              Final norm requirement for the Newton direction (see [1],
              argument EPS). It must be bigger than the machine
              precision epsmac (computed by FSQP). If the user does
              not have a good feeling of what value should be chosen,
              a very small number could be provided and IPRINT = 2 be
              selected so that the user would be able to keep track of
              the process of optimization and terminate FSQP at
              appropriate time.

      TOL2    DOUBLE PRECISION
              Corresponds to argument EPSEQN in [1].
              Maximum violation of nonlinear equality constraints
              allowed by the user at an optimal point. It is in effect
              only if NEQN > 0 and must be bigger than the machine
              precision epsmac (computed by FSQP).

Workspace

      IWORK   INTEGER array, dimension (LIWORK)
              Corresponds to argument IW in [1].

      LIWORK  INTEGER
              Corresponds to argument IWSIZE in [1].
              The length of array IWORK. It must be at least as big as
              6*NPARAM + 8*max(1,NINEQ+NEQ) + 7*max(1,NF) + 30. This
              estimate is usually very conservative and the smallest
              suitable value will be displayed if the user-supplied
              value is too small.

      DWORK   DOUBLE PRECISION array, dimension (LDWORK)
              Corresponds to argument W in [1].
              On exit, it will contain estimates of Lagrange
              multipliers at the end of execution. These multipliers
              will be placed in the first NPARAM + NINEQ + NEQ + NFF
              entries; where NFF = 0 if (in mode) A = 0 and NF = 1, and
              NFF = NF otherwise. See [1] for details.

      LDWORK  INTEGER
              Corresponds to argument NWSIZE in [1].
              The length of array DWORK. It must be at least as big as
              4*SQR(NPARAM) + 5*MAX(1,NINEQ+NEQ)*NPARAM +
              3*MAX(1,NF)*NPARAM + 26*(NPARAM+MAX(1,NF)) +
              45*MAX(1,NINEQ+NEQ) + 100.
              This estimate is usually very conservative and the
              smallest suitable value will be displayed if the user-
              supplied value is too small.

Error Indicator

      INFO    (output) INTEGER
              Corresponds to argument INFORM in [1].
              INFO = 0: Normal termination of execution.
              INFO = 1: The user-provided initial guess is infeasible
                 for linear constraints and FFSQP is unable to generate
                 a point satisfying all these constraints.
              INFO = 2: The user-provided initial guess is infeasible
                 for nonlinear inequality constraints and linear
                 constraints; and FFSQP is unable to generate a point
                 satisfying all these constraints.
              INFO = 3: The maximum number miter of iterations has
                 been reached before a solution is obtained.
              INFO = 4: The line search fails to find a new iterate
                 (trial step size being smaller than the machine
                 precision epsmac computed by FFSQP).
              INFO = 5: Failure of the QP solver in attempting to
                 construct d0. A more robust QP solver may succeed.
              INFO = 6: Failure of the QP solver in attempting to
                 construct d1 . A more robust QP solver may succeed.
              INFO = 7: Input data are not consistent (with printout
                 indicating the error).
              INFO = 8: Two consecutive iterates are numerically
                 equivalent before a stopping criterion is satisfied.
              INFO = 9: One of the penalty parameters exceeded
                 BIGBND. The algorithm is having trouble satisfying a
                 nonlinear equality constraint.

Method

      If the initial guess provided by the user is infeasible for
      nonlinear inequality constraints and linear constraints, FFSQP
      first generates a point satisfying all these constraints by
      iterating on the problem of minimizing the maximum of these
      constraints.

      Then, using Mayne-Polak's scheme, nonlinear equality
      constraints are turned into nonlinear inequality constraints
      and the original objective function is replaced by a modified
      objective function.

      After obtaining feasibility, either (i) an Armijo-type line
      search may be used (algorithm FFSQP-AL), yielding a monotone
      decrease of the objective function at each iteration; or (ii) a
      nonmonotone line search may be selected (algorithm FFSQP-NL),
      forcing a decrease of the objective function within at most four
      iterations.

      See [1] for further details.

References

      [1] J. L. Zhou, A. L. Tits and C. T. Lawrence
          User's Guide for FFSQP Version 3.7 : A Fortran Code for
          Solving Optimization Programs, Possibly Minimax, with General
          Inequality Constraints and Linear Equality Constraints,
          Generating Feasible Iterates
          Institute for Systems Research, University of Maryland,
          Technical Report SRC-TR-92-107r5, 1997.
          (Available at http://gachinese.com/aemdesign/FSQPframe.htm)

Further Comments

      None

Example

Program Text

      None

Program Data

      None

Program Results

      None

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