e04kfc (handle_solve_bounds_foas) : NAG Library CL Interface, Mark 27

Note: this function uses optional parameters to define choices in the problem specification and in the details of the algorithm. If you wish to use default settings for all of the optional parameters, you need only read Sections 1 to 10 of this document. If, however, you wish to reset some or all of the settings please refer to Section 11 for a detailed description of the algorithm and to Section 12 for a detailed description of the specification of the optional parameters.

e04kfc is a solver from the NAG optimization modelling suite for bound-constrained large-scale Nonlinear Programming (NLP) problems. It is a first-order active-set method (FOAS) that has low memory requirements and thus is suitable for very large-scale problems.

The function may be called by the names: e04kfc or nag_opt_handle_solve_bounds_foas.

e04kfc solves large-scale bound-constrained nonlinear optimization problems of the form

\underset{x \in R^{n}}{minimize} f (x) subject to ℓ_{x} \leq x \leq u_{x},

(1)

where

n

is the number of decision variables,

ℓ_{x}

,

u_{x}

, and

x

are

n

-dimensional vectors, and the nonlinear objective function

f (x)

is assumed to be sufficiently smooth.

The solver is a first-order method (i.e., uses only first derivatives) that has very low memory requirements and, therefore, is suitable for very large bound-constrained problems. It is based on an active-set method coupled to a nonmonotone projected gradient algorithm (NPG), nonlinear conjugate gradient method (CG) and its limited-memory variant (LCG). The active-set method is based on alternating between both solvers, the NPG step handles the box constraints and identifies a suitable search space while the CG step explores it for a solution.

For a detailed description of the algorithm see Section 11. Under standard assumptions on the problem (smoothness of the first derivative of the objective) the algorithm converges to a local solution or to a critical point.

e04kfc serves as a solver for problems stored as a handle. The handle points to an internal data structure which defines the problem and serves as a means of communication for functions in the NAG optimization modelling suite. First, the problem handle is initialized by calling e04rac. Then some of the functions e04rec, e04rfc, e04rgc or e04rhc may be called to formulate the objective and to define (simple) box constraints for the problem. Once the problem is fully described, the handle may be passed to the solver e04kfc. When the handle is no longer needed, e04rzc should be called to destroy it and deallocate the memory held within. See Section 4.1 in the E04 Chapter Introduction for more details about the NAG optimization modelling suite.

The algorithm behaviour can be modified by various optional parameters (see Section 12) which can be set by e04zmc and e04zpc anytime between the initialization of the handle by e04rac and a call to the solver. Once the solver has finished, options may be modified for the next solve. The solver may be called repeatedly with various starting points and/or optional parameters. Option getter e04znc can be called to retrieve the current value of any option.

The optional parameter

Task

may be used to switch the problem to maximization, while

FOAS Estimate Derivatives

can be used to complete missing elements from the gradient. Optional parameter

Verify Derivatives

may help verify the correctness of the gradient vector before starting to solve a problem.

Several options may have significant impact on the performance of the solver. Even if the defaults were chosen to suit the majority of problems, it is recommended that you experiment in order to find the most suitable set of options for a particular problem, see Sections 11 and 12 for further details.

Dai Y-H and Kou C-X (2013) A Nonlinear Conjugate Gradient Algorithm with an Optimal Property and an Improved Wolfe Line Search SIAM J. Optim. 23(1) 296–320

Gill P E and Leonard M W (2003) Limited-Memory Reduced-Hessian Methods for Large-Scale Unconstrained Optimization SIAM J. Optim. 14(2) 380–401

Hager W W and Zhang H (2005) A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search SIAM J. Optim. 16(1) 170–192

Hager W W and Zhang H (2006a) Algorithm 851: CG DESCENT, a Conjugate Gradient Method with Guaranteed Descent ACM Trans. Math. Software 32(1) 113–137

Hager W W and Zhang H (2006b) A New Active Set Algorithm for Box Constrained Optimization SIAM J. Optim. 17(2) 525–557

Hager W W and Zhang H (2013) The Limited Memory Conjugate Gradient Method SIAM J. Optim. 23(4) 2150–2168

Nocedal J and Wright S J (2006) Numerical Optimization (2nd Edition) Springer Series in Operations Research, Springer, New York

The accuracy of the solution is determined by optional parameters

FOAS Stop Tolerance

and

FOAS Rel Stop Tolerance

. If

fail . code =

NE_NOERROR on exit, the returned point satisfies (6) or (7) to the defined accuracy.

Please refer to Section 11.4 and the description of the particular options in Section 12.

e04kfc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

e04kfc makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.

Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

The solver can print information to give an overview of the problem and of the progress of the computation. The output may be sent to two independent streams (files) which are set by optional parameters

Print File

and

Monitoring File

. Optional parameters

Print Level

,

Monitoring Level

,

Print Solution

and

Print Options

determine the exposed level of detail. This allows, for example, a detailed log file to be generated while the condensed information is displayed on the screen.

By default (

Print File = 6

,

Print Level = 2

), the following sections are printed to the standard output:

Header
Optional parameters list (if $Print Options = YES$ )
Problem statistics
Verification of derivatives (if $Verify Derivatives = YES$ )
Iteration log
Summary
Solution (if $Print Solution = YES$ )

Header

The header is a message indicating the start of the solver. It should look like:

-------------------------------------------------------------------------------
 E04KF, First-order method for bound-constrained problems
-------------------------------------------------------------------------------

Optional parameters list

The list shows all options of the solver, each displayed on one line. The output contains the option name, its current value and an indicator for how it was set. The options unchanged from the default setting are noted by ‘d’, options you set are noted by ‘U’, and options reset by the solver are noted by ‘S’. Note that the output format is compatible with the file format expected by e04zpc. The output might look as follows:

Begin of Options
    Print File                    =                   6     * d
    Print Level                   =                   2     * U
    Print Options                 =                 Yes     * d
    Print Solution                =                 All     * U
    Monitoring File               =                   9     * U
    Monitoring Level              =                   3     * U
    Foas Monitor Frequency        =                   0     * d
    Foas Print Frequency          =                   5     * U
End of Options

Problem statistics

If

Print Level \geq 2

, statistics on the problem is printed, for example:

 Problem Statistics
   No of variables                  8 (+1 disabled, +2 fixed)
     free (unconstrained)           3
     bounded                        2
   Objective function       Nonlinear

Verification of derivatives

If

Verify Derivatives = YES

, then the user-supplied nonlinear objective gradient is verified. If

Print Level < 5

, then it will only check those entries related to the actual search space and compare to a finite-difference approximation, it might look as follows:

 Derivative checker
     idx test        fdx     approx  rel error
       3 Fail   3.56E+02   3.57E+02   2.80E-03
       4  Ok    1.80E+02   1.80E+02   6.05E-09
       5  Ok    0.00E+00   0.00E+00   0.00E+00

On the other hand, if

Print Level = 5

, it will print one line for each gradient entry and the output should be similar to:

 Derivative checker
     idx test        fdx     approx  rel error
       3 Fail   3.56E+02   3.57E+02   2.80E-03
       4  Ok    1.80E+02   1.80E+02   6.05E-09
       5  Ok    0.00E+00   0.00E+00   0.00E+00
       7 Skip         FD         --         --
       2 Skip    BLX=BUX         --         --
       6 Skip    BLX=BUX         --         --
       1 Skip   disabled         --         --

Iteration log

If

Print Level \geq 2

, the solver prints the status of every

k

th iteration, specified using the optional parameter

FOAS Print Frequency = k

, with default value of

k = 1

. It will also print status information regarding the switch between NPG, CG and LCG. It will also print status information regarding the switch between NPG, CG and LCG.

If

Print Level = 2

, the output shows the iteration number (

0

represents the starting point), the current objective value, and optimality measures (norm of inactive gradient and the norm of the projected direction). Note that all these values for the last iteration are also available in rinfo. The output might look as follows:

-------------------------------------------------------------------------------
  iters |  objective |  optim  |   dir
-------------------------------------------------------------------------------
      20  7.71339E-01  1.22E-01  1.70E+00
      25  7.06709E-01  1.75E+00  1.54E+00
      30  7.06709E-01  1.75E+00  1.54E+00
      35  6.82989E-01  1.35E+00  1.35E+00
      30  6.55834E-01  3.26E+00  1.67E+00

If

Print Level = 3

, the solver also prints for each iteration the progress score, iteration type, size or type of performed step, accumulated number of objective function and gradient calls. The output takes the following form:

-------------------------------------------------------------------------------
  iters |  objective |  optim  |   dir   | progrss | it|   step  |    nf|    ng
-------------------------------------------------------------------------------
       0  1.10354E+02  0.00E+00  6.81E+01  1.00E+00       Start        1      1
       1  3.29016E+01  1.45E+01  1.45E+01  5.36E+00 NPG  1.39E-02      8      2
       2  3.29016E+01  1.45E+01  1.45E+01  5.36E+00     Switch CG      8      2
       3  1.84090E+01  2.48E+01  2.48E+01  2.85E+00  CG  2.42E-02     11      4
       4  1.84090E+01  2.48E+01  2.48E+01  2.85E+00      Restart      11      4
       5  9.48087E+00  1.25E+01  1.25E+01  1.45E+00  CG  1.49E-02     13      5
       6  4.20527E+00  8.28E+00  8.22E-01  2.51E+00  CG  3.61E-02     15      6
       7  7.20453E-01  3.08E+00  1.62E+00  9.70E+00 LCG  7.21E-01     17      7

If

Print Level > 3

, each iteration produces more information that expands over several lines. This additional information can contain:

The sizes of gradient in full space and subspace;
The quadratic model prediction error and counter;
The amount of search direction restarts;
The extreme eigenvalues of the basis matrix for the subspace;
Information and size of the active-set;
Whether the orthogonal property holds;
The action to take for the next iteration (switch solver);
The search space size;
The amount of memory vectors used.

The output might look as follows:

-----  Iteration details (CG)  ---------
Size of gradient                2.64E+01
Size of subspace gradient       2.64E+01
Eigenvalues R range   6.80E-01  6.80E-01
Orthogonal property                 lost
Action                     Switch to LCG
----------------------------------------
-----  LCG solver details  -------------
Search space has            2 element(s)
Memory vectors                   2 (  2)
----------------------------------------

Summary

Once the solver finishes, a detailed summary is produced:

-------------------------------------------------------------------------------
Status: converged, an optimal solution was found
-------------------------------------------------------------------------------
Value of the objective             3.55353E-14
Norm of inactive gradient          2.53812E-07
Norm of projected direction        1.68481E-07
Iterations                                  32
Function evaluations                        80
FD func. evaluations                         5
Gradient evaluations                        48
  NPG function calls                         0
  NPG gradient calls                         0
  CG function calls                         11
  CG gradient calls                          9
  LCG function calls                        69
  LCG gradient calls                        39
-------------------------------------------------------------------------------

It starts with a status line of the overall result, followed by the final objective value as well as the gradient (unconstrained problem) or inactive gradient and projected direction (constrained problem) norms. If

Print Level > 1

or

Monitoring Level > 1

, it will additionally report iteration count, objective function and gradient call information.

Optionally, if

Stats Time = YES

, the timings for the user-supplied objective are displayed. It might look as follows:

Timing
  Total time spent                       12.54 sec
  Total time in obj function              3.01 sec ( 24.0%)
  Total time in obj gradient              5.28 sec ( 42.1%)

Solution

If

Print Solution = YES

, the values of the primal variables are printed, furthermore if the problem is constrained, also the dual variables (see Lagrangian Multipliers) and their bounds are reported. It might look as follows:

 Primal variables:
   idx   Lower bound       Value       Upper bound
     1     Disabled         NaN          Disabled
     2   5.00000E+00    5.00000E+00    5.00000E+00
     3  -1.00000E+00    8.00000E-01    8.00000E-01
     4  -2.00000E+00    6.40000E-01    2.00000E+00
     5   2.00000E+00    2.00000E+00    9.00000E+00
     6  -1.00000E+00   -1.00000E+00   -1.00000E+00
     7   0.00000E+00    0.00000E+00    1.00000E+00
 
 Box bounds dual variables:
   idx   Lower bound       Value       Upper bound       Value
     1     Disabled         NaN          Disabled         NaN
     2   5.00000E+00    0.00000E+00    5.00000E+00    0.00000E+00
     3  -1.00000E+00    0.00000E+00    8.00000E-01    4.00000E-01
     4  -2.00000E+00    0.00000E+00    2.00000E+00    0.00000E+00
     5   2.00000E+00    0.00000E+00    9.00000E+00    0.00000E+00
     6  -1.00000E+00    0.00000E+00   -1.00000E+00    0.00000E+00
     7   0.00000E+00    0.00000E+00    1.00000E+00    0.00000E+00

In this example, we minimize in

ℝ^{2}

the Rosenbrock function over simple bounds on the variables. The problem to solve is

\begin{array}{rcl} \underset{x \in ℝ^{2}}{minimize} f (x) = {(1 - x_{1})}^{2} + 100 {(x_{2} - x_{1}^{2})}^{2} & subject to & - 1 \leq x_{1} \leq 0.8, \\ - 2 \leq x_{2} \leq 2 . \end{array}

The initial guess is

x_{0} = {(- 1.5, 1.9)}^{T}

, and the expected solution point is

x^{*} = {(0.8, 0.64)}^{T}

with objective value

f (x^{*}) = 0.04

.

Program Text (e04kfce.c)

Program Results (e04kfce.r)

This section contains the description of the underlying algorithms used in e04kfc, a first-order active-set (FOAS) method with very low memory requirements suitable for large-scale bound-constrained nonlinear optimization problems. For further details, see Hager and Zhang (2006b) and references therein.

Active-set is a useful method to tackle problems with bound constraints. It derives its name from the partitioning of the search space: elements of

x

that are fixed at a bound (

x_{i} = ℓ_{i}

or

x_{i} = u_{i}

), are said to be active, while the rest of the elements of

x

are said to be inactive. The goal of this method is to estimate which variables will be active at the solution point while optimizing over the inactive components.

e04kfc consists of a constrained solver, an unconstrained solver and a set of rules to switch between them. The constrained solver (nonmonotone projected gradient, NPG) has two purposes: while solving (1) it tries to guess which components of

x

are active at a solution. Once a reasonable guess is available, control is transferred to an unconstrained solver (conjugate gradient method, CG, and its limited-memory variant, LCG) that operates only over the inactive elements of

x

but convergence is much faster that NPG.

The following is an outline of the implemented algorithm.

FOAS Algorithm

(i)Make initial point $x_{0}$ feasible.
(ii)Loop until stopping criteria are satisfied
1. (a)Constrained problem (NPG): start solving constrained problem (1) and try to identify a suitable inactive search space; if found, then go to (ii)(b).
2. (b)Unconstrained subproblem (CG): solve unconstrained problem (4) over the elements of $x$ marked inactive. If elements of $x$ become newly active or it is deemed that some active elements should be explored, then go back to (ii)(a).

By setting

Ω = {x \in ℝ^{n} : ℓ_{x} \leq x \leq u_{x}}

, the constrained problem (1) can be stated as

\min_{x \in Ω} f (x)

and can be solved using NPG which is an iterative method of the form

x_{k + 1} = x_{k} + α_{k} d_{Ω} (x_{k}),

(2)

with step size

α_{k}

and projection direction

d_{Ω} (x)

.

The projected direction,

d_{Ω} (x)

, is obtained by a projection of the objective gradient

g (x)

onto

Ω

:

d_{Ω} (x) = P_{Ω} (x - g (x)) - x,

(3)

with

P_{Ω}

the Euclidean projection operator over the box

Ω

. Note that if

Ω = ℝ^{n}

, then

d_{Ω} (x_{k}) = - g (x_{k})

and the method reduces to nonmonotone steepest descent.

The step size

α_{k} > 0

is chosen to guarantee global convergence, i.e., guarantee sufficient progress at each iteration using the nonmonotone Armijo condition

f (x_{k + 1}) \leq f_{k}^{R} + c α_{k} {g (x_{k})}^{T} d_{Ω} (x_{k}),   with ​ 0 < c < 1,

where

f_{k}^{R}

is a reference objective function value for iteration

k

, possibly

f_{k}^{R} = f (x_{k})

. The step size is estimated using a nonmonotone backtracking technique. As soon as a suitable inactive set is identified (see Hager and Zhang (2006b)), the control is transferred to CG (LCG) to accelerate the convergence.

The exploration of the inactive search space is performed using the CG method which is an iterative method to solve

\min_{x \in ℝ^{n}} f (x)

(4)

and each iterate is updated using the expression

x_{k + 1} = x_{k} + α_{k} d_{k},

(5)

where

d_{k}

is a direction of descent that combines the current gradient

g (x)

and a scaled version of the previous direction,

β_{k} d_{k - 1}

, with

β_{k} \in ℝ

,

d_{k + 1} = - g (x_{k + 1}) + β_{k} d_{k}, d_{0} = - g (x_{0}) .

The step size

α_{k} > 0

is chosen to ensure global convergence and is achieved by satisfying the weak Wolfe conditions

f (x_{k + 1}) - f (x_{k}) \leq δ α_{k} {g (x_{k})}^{T} d_{k}, {g (x_{k + 1})}^{T} d_{k} \geq σ {g (x_{k})}^{T} d_{k},

with

0 < δ < σ < 1

or the approximate Wolfe conditions (see Hager and Zhang (2005))

ε ε

,

\begin{matrix} (2 δ - 1) {g (x_{k})}^{T} d_{k} \geq {g (x_{k + 1})}^{T} d_{k}, & {g (x_{k + 1})}^{T} d_{k} \geq σ {g (x_{k})}^{T} d_{k},   and \\ {g (x_{k + 1})}^{T} d_{k} \leq {g (x_{k})}^{T} d_{k} + ε_{k}, \end{matrix}

with

δ < \min (0.5, σ)

and

ε_{k} > 0

. The line search performed by e04kfc will always satisfy either/or both stated conditions. The line search builds a series of nested intervals that contains a point satisfying the above sufficient decrease requirements.

It is important to note that when

Ω \neq ℝ^{n}

, then the used gradient is actually the inactive gradient, it is the gradient,

g (x_{k})

, but with zeros in the same positions where the elements of

x_{k}

are marked as active.

The performance of the CG method can degrade when orthogonality is lost between consecutive search directions. Therefore, e04kfc uses a limited number of previous search directions to detect and restore orthogonality. When the current search direction is no longer orthogonal, it is discarded and a quasi-Newton variant known as limited-memory CG (LCG) is used to build a new search direction orthogonal to the explored subspace.

The available memory is used to build an approximation of the Hessian and the new search direction is estimated using

d_{k} = {- H}_{k} g (x_{k}) .

The quasi-Newton Hessian approximation

H_{k}

, is updated at each iteration using the BFGS update formula, see Nocedal and Wright (2006).

Experiments in literature indicate that an adequate range for the quasi-Newton (amount of vectors used to build

H_{k}

) lies between

7

and

20

. The default value for the maximum amount of vectors used in e04kfc is

11

and can be changed via the

FOAS Memory

.

A point is considered a solution when there are no feasible descent directions to use. Under this circumstance the function will stop, declaring to have found a solution.

If the problem is unconstrained, the function declares to have found a solution and stops when the first-order optimality condition is met within the defined absolute or relative tolerances (

ε_{tol} \geq 0

,

ε_{rel} \geq 0

),

{‖ g (x_{k}) ‖}_{p} \leq \max (ε_{tol}, ε_{rel} {‖ g (x_{0}) ‖}_{p}),

(6)

where

p

can designate either the Euclidean or Infinity (default) norms.

On the other hand, if the problem is constrained, the function characterizes to have found a solution when the first-order condition is satisfied for the projected direction,

d_{Ω}

, to the defined tolerances,

{‖ d_{Ω} (x_{k}) ‖}_{p} \leq \max (ε_{tol}, ε_{rel} {‖ d_{Ω} (x_{0}) ‖}_{p}) .

(7)

In the unconstrained case we have

Ω = ℝ^{n}

and both stopping criteria (6) and (7) coincide.

The stopping tolerances can be changed using the optional parameters

FOAS Stop Tolerance

and

FOAS Rel Stop Tolerance

, while the norm used can be set by using the optional parameter

FOAS Tolerance Norm

, see Section 12 for details. If these parameters are set too small in relation to the complexity and scaling of the problem, the function can terminate with

fail . code =

NE_NO_IMPROVEMENT, NE_TOO_MANY_ITER or NW_NOT_CONVERGED.

Progress of the solver towards a solution is monitored using two criteria. The first one evaluates how poor the actual step has been and is estimated via

{- α}_{k} \frac{\nabla f {(x_{k})}^{T} d_{k}}{| f (x_{k}) |} < ε_{prog},

(8)

where

α_{k}

is given by the line search. If the above relation is consistently satisfied, then the solver stops with

fail . code =

NE_NO_IMPROVEMENT. The tolerance

ε_{prog}

is set using the optional parameter

FOAS Progress Tolerance

. The second criteria monitors the rate of convergence using

\frac{| f (x_{k - 1}) - f (x_{k}) |}{| f (x_{k}) |} < α_{k} ‖ \nabla f (x_{k}) ‖ ε_{slow} .

(9)

As with the first criteria, if the previous relation is deemed permanent, then the solver stops with

fail . code =

NW_NOT_CONVERGED. The tolerance

ε_{slow}

can be changed using the optional parameter

FOAS Slow Tolerance

.

It is often useful to have access to the Lagrangian multipliers (dual variables) associated with the constraints if there are any defined. In the case where only simple bounds are present, the multipliers directly relate to the values of the gradient at the solution. The multipliers of the active bounds are the absolute values of the associated elements of the gradient. The multipliers of the inactive bounds are always zero.

The multipliers based on the final gradient value (or its finite-difference approximation) can be retrieved by calling e04rxc with the command string

cmdstr =' Dual Variables'

. The format is the same as for other functions, see Section 3.1 in e04svc. Note that if the problem has not fully converged, the provided approximation might be quite crude.

Several optional parameters in e04kfc define choices in the problem specification or the algorithm logic. In order to reduce the number of formal arguments of e04kfc these optional parameters have associated default values that are appropriate for most problems. Therefore, you need only specify those optional parameters whose values are to be different from their default values.

The remainder of this section can be skipped if you wish to use the default values for all optional parameters.

The optional parameters can be changed by calling e04zmc anytime between the initialization of the handle and the call to the solver. Modification of the optional parameters during intermediate monitoring steps is not allowed. Once the solver finishes, the optional parameters can be altered again for the next solve.

The option values may be retrieved by e04znc.

The following is a list of the optional parameters available. A full description of each optional parameter is provided in Section 12.1.

For each option, we give a summary line, a description of the optional parameter and details of constraints.

The summary line contains:

All options accept the value

DEFAULT

to return single options to their default states.

Keywords and character values are case and white space insensitive.

This special keyword may be used to reset all optional parameters to their default values. Any value given with this keyword will be ignored.

This option indicates whether to check for and estimate missing entries of the user-supplied gradient vector. Since the associated cost of estimating missing elements can be high, this option should only be used if strictly necessary. In general terms, if the gradient is not provided (in its entirety or for arbitrary points) potential degradation in the progress of the solver is to be expected. Depending on the complexity of the objective, the function may not achieve the desired optimality accuracy or even terminate with no possible further progress error

fail . code =

NE_NO_IMPROVEMENT, it is advisable to increase the values of

FOAS Stop Tolerance

and

FOAS Rel Stop Tolerance

when using this option.

Missing elements from the gradient vector are estimated by finite-differences using the perturbation interval specified by the optional parameter

FOAS Finite Diff Interval

.

If

FOAS Estimate Derivatives = NO

, the entries are not checked and all derivative elements need to be provided.

Constraint:

FOAS Estimate Derivatives = YES

or

NO

.

Specifies the relative perturbation size used to estimate a derivative using the forward (or backward) finite-difference method. Setting the value too small or too big may lead e04kfc to terminated with

fail . code =

NE_NO_IMPROVEMENT or NE_USER_NAN.

Constraint:

10^{- 12} \leq FOAS Finite Diff Interval \leq 10^{- 1}

.

This parameter sets the maximum number of iterations to be performed by e04kfc. Setting the option too low might lead to

fail . code =

NE_TOO_MANY_ITER.

Constraint:

FOAS Iteration Limit \geq 1

.

This parameter specifies the maximum number of memory vectors to use in the LCG solver.

Constraint:

0 \leq FOAS Memory \leq 100

.

This parameter specifies the frequency on which to call the monitor function monit. If zero, the monitor function will not be called.

Constraint:

FOAS Monitor Frequency \geq 0

.

This parameter specifies the frequency with which to print information regarding each iteration to

Print File

and/or

Monitoring File

. By default, it will print information of every iteration.

Constraint:

FOAS Print Frequency \geq 1

.

Specifies the tolerance for

ε_{prog}

(see (8)) for which the function characterises a poor rate of progress given that it deems to be far from a solution. If this behaviour is persistent, then the function asserts that no substantial further progress can be achieved and the process is terminated with

fail . code =

NE_NO_IMPROVEMENT. Setting a high tolerance can lead to misinterpret reasonable progress for unsatisfactory progress or even issue a premature stop, see (8) in Section 11.4.

Constraint:

0 < FOAS Progress Tolerance < 1

.

This parameter sets the value of

ε_{rel}

which specifies the relative tolerance for the convergence measures in the stopping criteria, see (6) and (7) in Section 11.4.

Constraint:

0 \leq ε_{rel} < 1

.

This factor specifies the frequency

nvar \times FOAS Restart Factor

with which the CG/LCG directions are replaced by the steepest descent direction (

d_{k} = - g_{k}

). Setting the value too small can potentially slow the convergence speed.

Constraint:

FOAS Restart Factor \geq 0

.

Specifies the tolerance for

ε_{slow}

(see (9)) for which the function characterises a slow rate of convergence. If this behaviour is deemed permanent, then the function asserts that no substantial improvement can be achieved and the process is terminated with

fail . code =

NW_NOT_CONVERGED. Setting a large tolerance can lead to incorrectly identifying a suboptimal solution, see (9) in Section 11.4.

Constraint:

FOAS Slow Tolerance > 0

.

This parameter sets the value of

ε_{tol}

which specifies the tolerance for the convergence measures in the stopping criteria, see (6) and (7) in Section 11.4.

Constraint:

0 \leq ε_{tol} < 1

.

This parameter specifies the norm used to measure some stopping metrics, such as optimality tolerances (see Section 11.4). It is possible to choose between 2-norm and ∞-norm. Solving problems using ∞-norm generally has lower computational costs than those based on 2-norm.

Constraint:

FOAS Tolerance Norm = INFINITY

or

TWO

.

This defines the ‘infinite’ bound

bigbnd

in the definition of the problem constraints. Any upper bound greater than or equal to

bigbnd

will be regarded as

+ \infty

(and similarly any lower bound less than or equal to

- bigbnd

will be regarded as

- \infty

). Note that a modification of this optional parameter does not influence constraints which have already been defined; only the constraints formulated after the change will be affected.

Constraint:

Infinite Bound Size \geq 1000

.

(See Section 3.1.1 in the Introduction to the NAG Library CL Interface for further information on NAG data types.)

If

i \geq 0

, the Nag_FileID number (as returned from x04acc) for the secondary (monitoring) output. If set to

- 1

, no secondary output is provided. The following information is output to the unit:

–a listing of the optional parameters;
–problem statistics, the iteration log and the final status as set by $Monitoring Level$ ;
–the solution if set by $Print Solution$ .

Constraint:

Monitoring File \geq - 1

.

This parameter sets the amount of information detail that will be printed by the solver to the secondary output. The meaning of the levels is the same as

Print Level

.

Constraint:

0 \leq Monitoring Level \leq 5

.

(See Section 3.1.1 in the Introduction to the NAG Library CL Interface for further information on NAG data types.)

If

i \geq 0

, the Nag_FileID number (as returned from x04acc, stdout as the default) for the primary output of the solver. If

Print File = - 1

, the primary output is completely turned off independently of other settings. The following information is output to the unit:

–a listing of optional parameters if set by $Print Options$ ;
–problem statistics, the iteration log and the final status from the solver as set by $Print Level$ ;
–the solution if set by $Print Solution$ .

Constraint:

Print File \geq - 1

.

This parameter defines how detailed information should be printed by the solver to the primary output.

$i$	Output
$0$	No output from the solver
$1$	Only the final status and the objective value
$2$	Problem statistics, one line per iteration showing the progress of the solution with respect to the convergence measures, final status and statistics
$3$	As level $2$ but each iteration line is longer and includes step length and progress measure
$4, 5$	As level $3$ but further details of each iteration are presented

Constraint:

0 \leq Print Level \leq 5

.

If

Print Options = YES

, a listing of optional parameters will be printed to the primary output.

Constraint:

Print Options = YES

or

NO

.

If

Print Solution = YES

, the final values of the solution vector are printed on the primary and secondary outputs.

Constraint:

Print Solution = YES

or

NO

.

This parameter allows you to turn on timings of various parts of the algorithm to give a better overview of where most of the time is spent. This might be helpful for a choice of different solving approaches. It is possible to choose between CPU and wall clock time. Choice

YES

is equivalent to

WALL CLOCK

.

Constraint:

Stats Time = YES

,

NO

,

CPU

or

WALL CLOCK

.

This parameter specifies the required direction of the optimization. If

Task = FEASIBLE POINT

, the objective function (if set) is ignored and the algorithm stops as soon as a feasible point is found. If no objective function is set,

Task

reverts to

FEASIBLE POINT

automatically.

Constraint:

Task = MINIMIZE

,

MAXIMIZE

or

FEASIBLE POINT

.

This parameter specifies a limit in seconds that the solver can use to solve one problem. If during the convergence check this limit is exceeded, the solver will terminate with

fail . code =

NE_TIME_LIMIT error message.

Constraint:

Time Limit > 0

.

This parameter specifies whether the function should perform numerical checks on the consistency of the user-supplied gradient function. If any discrepancies are found,

fail . code =

NE_DERIV_ERRORS is returned. It is recommended that such checks are enabled when first developing the formulation of the problem, however, the verification process results in a significant increase of the number of the function evaluations and thus it shouldn't be used in production code.

Constraint:

Verify Derivatives = YES

or

NO

.

$0$	Objective function value $f (x)$ .
$1$	Norm of inactive gradient, the objective gradient over the current search space. If the problem is unconstrained, then elements $1$ – $3$ coincide. See Section 11.4 for details.
$2$	Norm of projected direction, used in (3), see Section 11.2.
$3$	Norm of objective gradient.
$4$	Last step size ( $α_{k}$ ) used in (2) and (5), see Section 11.2 and Section 11.3.
$5$	Progress score, a positive value that measures the progress of the solver. A low score close to zero indicates poor progress, see (8) in Section 11.4.
$6$ – $99$	Reserved for future use.

$0$	Number of function evaluations performed by NPG, see Section 11.2.
$1$	Number of gradient evaluations performed by NPG.
$2$	Number of function evaluations performed by CG, see Section 11.3.
$3$	Number of gradient evaluations performed by CG.
$4$	Number of function evaluations performed by LCG.
$5$	Number of gradient evaluations performed by LCG.
$6$	Number of function evaluations used by the finite difference method to estimate missing components of the gradient.
$7$	Number of iterations.
$8$	Total time spent in the solver (including user-supplied function calls).
$9$	Total time spent in user-supplied objective function.
$10$	Total time spent in user-supplied objective gradient.
$11$ – $99$	Reserved for future use.

NAG CL Interface
e04kfc (handle_solve_bounds_foas)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

9.1 Description of the Printed Output

10 Example

10.1 Program Text

10.2 Program Results

11 Algorithmic Details

11.1 Active-Set Method and Algorithm Outline

11.2 Constrained Subproblem (NPG)

11.3 Unconstrained Subproblem (CG)

11.4 Stopping Criteria

11.5 A Note About Lagrangian Multipliers

12 Optional Parameters

12.1 Description of the Optional Parameters

NAG CL Interfacee04kfc (handle_​solve_​bounds_​foas)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

9.1 Description of the Printed Output

10 Example

10.1 Program Text

10.2 Program Results

11 Algorithmic Details

11.1 Active-Set Method and Algorithm Outline

11.2 Constrained Subproblem (NPG)

11.3 Unconstrained Subproblem (CG)

11.4 Stopping Criteria

11.5 A Note About Lagrangian Multipliers

12 Optional Parameters

12.1 Description of the Optional Parameters

NAG CL Interface
e04kfc (handle_solve_bounds_foas)