## Copyright (C) 2005-2018 John W. Eaton
## Copyright (C) 2013-2018 Arun Giridhar
##
## This file is part of Octave.
##
## Octave is free software: you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## Octave is distributed in the hope that it will be useful, but
## WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with Octave; see the file COPYING.  If not, see
## <https://www.gnu.org/licenses/>.

## -*- texinfo -*-
## @deftypefn  {} {[@var{x}, @var{obj}, @var{info}, @var{iter}, @var{nf}, @var{lambda}] =} sqp (@var{x0}, @var{phi})
## @deftypefnx {} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g})
## @deftypefnx {} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}, @var{h})
## @deftypefnx {} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}, @var{h}, @var{lb}, @var{ub})
## @deftypefnx {} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}, @var{h}, @var{lb}, @var{ub}, @var{maxiter})
## @deftypefnx {} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}, @var{h}, @var{lb}, @var{ub}, @var{maxiter}, @var{tol})
## Minimize an objective function using sequential quadratic programming (SQP).
##
## Solve the nonlinear program
## @tex
## $$
## \min_x \phi (x)
## $$
## @end tex
## @ifnottex
##
## @example
## @group
## min phi (x)
##  x
## @end group
## @end example
##
## @end ifnottex
## subject to
## @tex
## $$
##  g(x) = 0 \qquad h(x) \geq 0 \qquad lb \leq x \leq ub
## $$
## @end tex
## @ifnottex
##
## @example
## @group
## g(x)  = 0
## h(x) >= 0
## lb <= x <= ub
## @end group
## @end example
##
## @end ifnottex
## @noindent
## using a sequential quadratic programming method.
##
## The first argument is the initial guess for the vector @var{x0}.
##
## The second argument is a function handle pointing to the objective function
## @var{phi}.  The objective function must accept one vector argument and
## return a scalar.
##
## The second argument may also be a 2- or 3-element cell array of function
## handles.  The first element should point to the objective function, the
## second should point to a function that computes the gradient of the
## objective function, and the third should point to a function that computes
## the Hessian of the objective function.  If the gradient function is not
## supplied, the gradient is computed by finite differences.  If the Hessian
## function is not supplied, a BFGS update formula is used to approximate the
## Hessian.
##
## When supplied, the gradient function @code{@var{phi}@{2@}} must accept one
## vector argument and return a vector.  When supplied, the Hessian function
## @code{@var{phi}@{3@}} must accept one vector argument and return a matrix.
##
## The third and fourth arguments @var{g} and @var{h} are function handles
## pointing to functions that compute the equality constraints and the
## inequality constraints, respectively.  If the problem does not have
## equality (or inequality) constraints, then use an empty matrix ([]) for
## @var{g} (or @var{h}).  When supplied, these equality and inequality
## constraint functions must accept one vector argument and return a vector.
##
## The third and fourth arguments may also be 2-element cell arrays of
## function handles.  The first element should point to the constraint
## function and the second should point to a function that computes the
## gradient of the constraint function:
## @tex
## $$
##  \Bigg( {\partial f(x) \over \partial x_1},
##         {\partial f(x) \over \partial x_2}, \ldots,
##         {\partial f(x) \over \partial x_N} \Bigg)^T
## $$
## @end tex
## @ifnottex
##
## @example
## @group
##             [ d f(x)   d f(x)        d f(x) ]
## transpose ( [ ------   -----   ...   ------ ] )
##             [  dx_1     dx_2          dx_N  ]
## @end group
## @end example
##
## @end ifnottex
## The fifth and sixth arguments, @var{lb} and @var{ub}, contain lower and
## upper bounds on @var{x}.  These must be consistent with the equality and
## inequality constraints @var{g} and @var{h}.  If the arguments are vectors
## then @var{x}(i) is bound by @var{lb}(i) and @var{ub}(i).  A bound can also
## be a scalar in which case all elements of @var{x} will share the same
## bound.  If only one bound (lb, ub) is specified then the other will
## default to (-@var{realmax}, +@var{realmax}).
##
## The seventh argument @var{maxiter} specifies the maximum number of
## iterations.  The default value is 100.
##
## The eighth argument @var{tol} specifies the tolerance for the stopping
## criteria.  The default value is @code{sqrt (eps)}.
##
## The value returned in @var{info} may be one of the following:
##
## @table @asis
## @item 101
## The algorithm terminated normally.
## All constraints meet the specified tolerance.
##
## @item 102
## The BFGS update failed.
##
## @item 103
## The maximum number of iterations was reached.
##
## @item 104
## The stepsize has become too small, i.e.,
## @tex
## $\Delta x,$
## @end tex
## @ifnottex
## delta @var{x},
## @end ifnottex
## is less than @code{@var{tol} * norm (x)}.
## @end table
##
## An example of calling @code{sqp}:
##
## @example
## function r = g (x)
##   r = [ sumsq(x)-10;
##         x(2)*x(3)-5*x(4)*x(5);
##         x(1)^3+x(2)^3+1 ];
## endfunction
##
## function obj = phi (x)
##   obj = exp (prod (x)) - 0.5*(x(1)^3+x(2)^3+1)^2;
## endfunction
##
## x0 = [-1.8; 1.7; 1.9; -0.8; -0.8];
##
## [x, obj, info, iter, nf, lambda] = sqp (x0, @@phi, @@g, [])
##
## x =
##
##   -1.71714
##    1.59571
##    1.82725
##   -0.76364
##   -0.76364
##
## obj = 0.053950
## info = 101
## iter = 8
## nf = 10
## lambda =
##
##   -0.0401627
##    0.0379578
##   -0.0052227
## @end example
##
## @seealso{qp}
## @end deftypefn

function [x, obj, info, iter, nf, lambda] = sqp (x0, objf, cef, cif, lb, ub, maxiter, tolerance)

  globals = struct (); # data and handles, needed and changed by subfunctions

  if (nargin < 2 || nargin > 8 || nargin == 5)
    print_usage ();
  endif

  if (! isvector (x0))
    error ("sqp: X0 must be a vector");
  endif
  if (rows (x0) == 1)
    x0 = x0';
  endif

  have_hess = 0;
  if (iscell (objf))
    switch (numel (objf))
      case 1
        obj_fun = objf{1};
        obj_grd = @(x) fd_obj_grd (x, obj_fun);
      case 2
        obj_fun = objf{1};
        obj_grd = objf{2};
      case 3
        obj_fun = objf{1};
        obj_grd = objf{2};
        obj_hess = objf{3};
        have_hess = 1;
      otherwise
        error ("sqp: invalid objective function specification");
    endswitch
  else
    obj_fun = objf;   # No cell array, only obj_fun set
    obj_grd = @(x) fd_obj_grd (x, obj_fun);
  endif

  ce_fun = @empty_cf;
  ce_grd = @empty_jac;
  if (nargin > 2)
    if (iscell (cef))
      switch (numel (cef))
        case 1
          ce_fun = cef{1};
          ce_grd = @(x) fd_ce_jac (x, ce_fun);
        case 2
          ce_fun = cef{1};
          ce_grd = cef{2};
        otherwise
          error ("sqp: invalid equality constraint function specification");
      endswitch
    elseif (! isempty (cef))
      ce_fun = cef;   # No cell array, only constraint equality function set
      ce_grd = @(x) fd_ce_jac (x, ce_fun);
    endif
  endif

  ci_fun = @empty_cf;
  ci_grd = @empty_jac;
  if (nargin > 3)
    ## constraint function given by user with possible gradient
    globals.cif = cif;
    ## constraint function given by user without gradient
    globals.cifcn = @empty_cf;
    if (iscell (cif))
      if (length (cif) > 0)
        globals.cifcn = cif{1};
      endif
    elseif (! isempty (cif))
      globals.cifcn = cif;
    endif

    if (nargin < 5 || (nargin > 5 && isempty (lb) && isempty (ub)))
      ## constraint inequality function only without any bounds
      ci_grd = @(x) fd_ci_jac (x, globals.cifcn);
      if (iscell (cif))
        switch (length (cif))
          case 1
            ci_fun = cif{1};
          case 2
            ci_fun = cif{1};
            ci_grd = cif{2};
          otherwise
           error ("sqp: invalid inequality constraint function specification");
        endswitch
      elseif (! isempty (cif))
        ci_fun = cif;   # No cell array, only constraint inequality function set
      endif
    else
      ## constraint inequality function with bounds present
      lb_idx = ub_idx = true (size (x0));
      ub_grad = - (lb_grad = eye (rows (x0)));
      if (isvector (lb))
        globals.lb = tmp_lb = lb(:);
        lb_idx(:) = tmp_idx = (lb != -Inf);
        globals.lb = globals.lb(tmp_idx, 1);
        lb_grad = lb_grad(lb_idx, :);
      elseif (isempty (lb))
        if (isa (x0, "single"))
          globals.lb = tmp_lb = -realmax ("single");
        else
          globals.lb = tmp_lb = -realmax;
        endif
      else
        error ("sqp: invalid lower bound");
      endif

      if (isvector (ub))
        globals.ub = tmp_ub = ub(:);
        ub_idx(:) = tmp_idx = (ub != Inf);
        globals.ub = globals.ub(tmp_idx, 1);
        ub_grad = ub_grad(ub_idx, :);
      elseif (isempty (ub))
        if (isa (x0, "single"))
          globals.ub = tmp_ub = realmax ("single");
        else
          globals.ub = tmp_ub = realmax;
        endif
      else
        error ("sqp: invalid upper bound");
      endif

      if (any (tmp_lb > tmp_ub))
        error ("sqp: upper bound smaller than lower bound");
      endif
      bounds_grad = [lb_grad; ub_grad];
      ci_fun = @(x) cf_ub_lb (x, lb_idx, ub_idx, globals);
      ci_grd = @(x) cigrad_ub_lb (x, bounds_grad, globals);
    endif

  endif   # if (nargin > 3)

  iter_max = 100;
  if (nargin > 6 && ! isempty (maxiter))
    if (isscalar (maxiter) && maxiter > 0 && fix (maxiter) == maxiter)
      iter_max = maxiter;
    else
      error ("sqp: invalid number of maximum iterations");
    endif
  endif

  tol = sqrt (eps);
  if (nargin > 7 && ! isempty (tolerance))
    if (isscalar (tolerance) && tolerance > 0)
      tol = tolerance;
    else
      error ("sqp: invalid value for TOLERANCE");
    endif
  endif

  ## Initialize variables for search loop
  ## Seed x with initial guess and evaluate objective function, constraints,
  ## and gradients at initial value x0.
  ##
  ## obj_fun   -- objective function
  ## obj_grad  -- objective gradient
  ## ce_fun    -- equality constraint functions
  ## ci_fun    -- inequality constraint functions
  ## A == [grad_{x_1} cx_fun, grad_{x_2} cx_fun, ..., grad_{x_n} cx_fun]^T
  x = x0;

  obj = feval (obj_fun, x0);
  globals.nfun = 1;

  c = feval (obj_grd, x0);

  ## Choose an initial NxN symmetric positive definite Hessian approximation B.
  n = length (x0);
  if (have_hess)
    B = feval (obj_hess, x0);
  else
    B = eye (n, n);
  endif

  ce = feval (ce_fun, x0);
  F = feval (ce_grd, x0);

  ci = feval (ci_fun, x0);
  C = feval (ci_grd, x0);

  A = [F; C];

  ## Choose an initial lambda (x is provided by the caller).
  lambda = 100 * ones (rows (A), 1);

  qp_iter = 1;
  alpha = 1;

  info = 0;
  iter = 0;
  # report ();  # Called with no arguments to initialize reporting
  # report (iter, qp_iter, alpha, __sqp_nfun__, obj);

  while (++iter < iter_max)

    ## Check convergence.  This is just a simple check on the first
    ## order necessary conditions.
    nr_f = rows (F);

    lambda_e = lambda((1:nr_f)');
    lambda_i = lambda((nr_f+1:end)');

    con = [ce; ci];

    t0 = norm (c - A' * lambda);
    t1 = norm (ce);
    t2 = all (ci >= 0);
    t3 = all (lambda_i >= 0);
    t4 = norm (lambda .* con);

    ## Normal convergence.  All constraints are satisfied
    ## and objective has converged.
    if (t2 && t3 && max ([t0; t1; t4]) < tol)
      info = 101;
      break;
    endif

    ## Compute search direction p by solving QP.
    g = -ce;
    d = -ci;

    old_lambda = lambda;
    [p, obj_qp, INFO, lambda] = qp (x, B, c, F, g, [], [], d, C,
                                    Inf (size (d)));

    info = INFO.info;

    ## FIXME: check QP solution and attempt to recover if it has failed.
    ##        For now, just warn about possible problems.

    id = "Octave:SQP-QP-subproblem";
    switch (info)
      case 2
        warning (id, "sqp: QP subproblem is non-convex and unbounded");
      case 3
        warning (id, "sqp: QP subproblem failed to converge in %d iterations",
                 INFO.solveiter);
      case 6
        warning (id, "sqp: QP subproblem is infeasible");
        lambda = old_lambda;  # The return value was size 0x0 in this case.
    endswitch

    ## Choose mu such that p is a descent direction for the chosen
    ## merit function phi.
    [x_new, alpha, obj_new, globals] = ...
        linesearch_L1 (x, p, obj_fun, obj_grd, ce_fun, ci_fun, lambda, ...
                       obj, globals);

    ## Evaluate objective function, constraints, and gradients at x_new.
    c_new = feval (obj_grd, x_new);

    ce_new = feval (ce_fun, x_new);
    F_new = feval (ce_grd, x_new);

    ci_new = feval (ci_fun, x_new);
    C_new = feval (ci_grd, x_new);

    A_new = [F_new; C_new];

    ## Set
    ##
    ## s = alpha * p
    ## y = grad_x L (x_new, lambda) - grad_x L (x, lambda})

    y = c_new - c;

    if (! isempty (A))
      t = ((A_new - A)'*lambda);
      y -= t;
    endif

    delx = x_new - x;

    ## Check if step size has become too small (indicates lack of progress).
    if (norm (delx) < tol * norm (x))
      info = 104;
      break;
    endif

    if (have_hess)

      B = feval (obj_hess, x);

    else
      ## Update B using a quasi-Newton formula.
      delxt = delx';

      ## Damped BFGS.  Or maybe we would actually want to use the Hessian
      ## of the Lagrangian, computed directly?
      d1 = delxt*B*delx;

      t1 = 0.2 * d1;
      t2 = delxt*y;

      if (t2 < t1)
        theta = 0.8*d1/(d1 - t2);
      else
        theta = 1;
      endif

      r = theta*y + (1-theta)*B*delx;

      d2 = delxt*r;

      ## Check if the next BFGS update will work properly.
      ## If d1 or d2 vanish, the BFGS update will fail.
      if (d1 == 0 || d2 == 0)
        info = 102;
        break;
      endif

      B = B - B*delx*delxt*B/d1 + r*r'/d2;

    endif

    x = x_new;

    obj = obj_new;

    c = c_new;

    ce = ce_new;
    F = F_new;

    ci = ci_new;
    C = C_new;

    A = A_new;

    # report (iter, qp_iter, alpha, __sqp_nfun__, obj);

  endwhile

  ## Check if we've spent too many iterations without converging.
  if (iter >= iter_max)
    info = 103;
  endif

  nf = globals.nfun;

endfunction


function [merit, obj, globals] = phi_L1 (obj, obj_fun, ce_fun, ci_fun, ...
                                         x, mu, globals)

  ce = feval (ce_fun, x);
  ci = feval (ci_fun, x);

  idx = ci < 0;

  con = [ce; ci(idx)];

  if (isempty (obj))
    obj = feval (obj_fun, x);
    globals.nfun += 1;
  endif

  merit = obj;
  t = norm (con, 1) / mu;

  if (! isempty (t))
    merit += t;
  endif

endfunction


function [x_new, alpha, obj, globals] = ...
   linesearch_L1 (x, p, obj_fun, obj_grd, ce_fun, ci_fun, lambda, obj, globals)

  ## Choose parameters
  ##
  ## eta in the range (0, 0.5)
  ## tau in the range (0, 1)

  eta = 0.25;
  tau = 0.5;

  delta_bar = sqrt (eps);

  if (isempty (lambda))
    mu = 1 / delta_bar;
  else
    mu = 1 / (norm (lambda, Inf) + delta_bar);
  endif

  alpha = 1;

  c = feval (obj_grd, x);
  ce = feval (ce_fun, x);

  [phi_x_mu, obj, globals] = phi_L1 (obj, obj_fun, ce_fun, ci_fun, x, ...
                                     mu, globals);

  D_phi_x_mu = c' * p;
  d = feval (ci_fun, x);
  ## only those elements of d corresponding
  ## to violated constraints should be included.
  idx = d < 0;
  t = - norm ([ce; d(idx)], 1) / mu;
  if (! isempty (t))
    D_phi_x_mu += t;
  endif

  while (1)
    [p1, obj, globals] = phi_L1 ([], obj_fun, ce_fun, ci_fun, ...
                                 x+alpha*p, mu, globals);
    p2 = phi_x_mu+eta*alpha*D_phi_x_mu;
    if (p1 > p2)
      ## Reset alpha = tau_alpha * alpha for some tau_alpha in the
      ## range (0, tau).
      tau_alpha = 0.9 * tau;  # ??
      alpha = tau_alpha * alpha;
    else
      break;
    endif
  endwhile

  x_new = x + alpha * p;

endfunction


function grd = fdgrd (f, x)

  if (! isempty (f))
    y0 = feval (f, x);
    nx = length (x);
    grd = zeros (nx, 1);
    deltax = sqrt (eps);
    for i = 1:nx
      t = x(i);
      x(i) += deltax;
      grd(i) = (feval (f, x) - y0) / deltax;
      x(i) = t;
    endfor
  else
    grd = zeros (0, 1);
  endif

endfunction


function jac = fdjac (f, x)

  nx = length (x);
  if (! isempty (f))
    y0 = feval (f, x);
    nf = length (y0);
    nx = length (x);
    jac = zeros (nf, nx);
    deltax = sqrt (eps);
    for i = 1:nx
      t = x(i);
      x(i) += deltax;
      jac(:,i) = (feval (f, x) - y0) / deltax;
      x(i) = t;
    endfor
  else
    jac = zeros  (0, nx);
  endif

endfunction


function grd = fd_obj_grd (x, obj_fun)

  grd = fdgrd (obj_fun, x);

endfunction


function res = empty_cf (x)

  res = zeros (0, 1);

endfunction


function res = empty_jac (x)

  res = zeros (0, length (x));

endfunction


function jac = fd_ce_jac (x, ce_fun)

  jac = fdjac (ce_fun, x);

endfunction


function jac = fd_ci_jac (x, cifcn)

  ## cifcn = constraint function without gradients and lb or ub
  jac = fdjac (cifcn, x);

endfunction


function res = cf_ub_lb (x, lbidx, ubidx, globals)

  ## combine constraint function with ub and lb
  if (isempty (globals.cifcn))
    res = [x(lbidx,1)-globals.lb; globals.ub-x(ubidx,1)];
  else
    res = [feval(globals.cifcn,x); x(lbidx,1)-globals.lb;
           globals.ub-x(ubidx,1)];
  endif

endfunction


function res = cigrad_ub_lb (x, bgrad, globals)

  cigradfcn = @(x) fd_ci_jac (x, globals.cifcn);

  if (iscell (globals.cif) && length (globals.cif) > 1)
    cigradfcn = globals.cif{2};
  endif

  if (isempty (cigradfcn))
    res = bgrad;
  else
    res = [feval(cigradfcn,x); bgrad];
  endif

endfunction

## Utility function used to debug sqp
function report (iter, qp_iter, alpha, nfun, obj)

  if (nargin == 0)
    printf ("  Itn ItQP     Step  Nfun     Objective\n");
  else
    printf ("%5d %4d %8.1g %5d %13.6e\n", iter, qp_iter, alpha, nfun, obj);
  endif

endfunction


################################################################################
## Test Code

%!function r = __g (x)
%!  r = [sumsq(x)-10;
%!       x(2)*x(3)-5*x(4)*x(5);
%!       x(1)^3+x(2)^3+1 ];
%!endfunction
%!
%!function obj = __phi (x)
%!  obj = exp (prod (x)) - 0.5*(x(1)^3 + x(2)^3 + 1)^2;
%!endfunction
%!
%!test
%!
%! x0 = [-1.8; 1.7; 1.9; -0.8; -0.8];
%!
%! [x, obj, info, iter, nf, lambda] = sqp (x0, @__phi, @__g, []);
%!
%! x_opt = [-1.717143501952599;
%!           1.595709610928535;
%!           1.827245880097156;
%!          -0.763643103133572;
%!          -0.763643068453300];
%!
%! obj_opt = 0.0539498477702739;
%!
%! assert (x, x_opt, 8*sqrt (eps));
%! assert (obj, obj_opt, sqrt (eps));

## Test input validation
%!error sqp ()
%!error sqp (1)
%!error sqp (1,2,3,4,5,6,7,8,9)
%!error sqp (1,2,3,4,5)
%!error sqp (ones (2,2))
%!error sqp (1, cell (4,1))
%!error sqp (1, cell (3,1), cell (3,1))
%!error sqp (1, cell (3,1), cell (2,1), cell (3,1))
%!error sqp (1, cell (3,1), cell (2,1), cell (2,1), ones (2,2),[])
%!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[], ones (2,2))
%!error sqp (1, cell (3,1), cell (2,1), cell (2,1),1,-1)
%!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[], ones (2,2))
%!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[],-1)
%!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[],1.5)
%!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[],[], ones (2,2))
%!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[],[],-1)
