#ifndef __LBFGS_H__
#define __LBFGS_H__

#ifndef LBFGS_FLOAT
#define LBFGS_FLOAT     64
#endif/*LBFGS_FLOAT*/

/*
 * Activate optimization routines for IEEE754 floating point values.
 */
#ifndef LBFGS_IEEE_FLOAT
#define LBFGS_IEEE_FLOAT    1
#endif/*LBFGS_IEEE_FLOAT*/

#if     LBFGS_FLOAT == 32
typedef float lbfgsfloatval_t;

#elif   LBFGS_FLOAT == 64
typedef double lbfgsfloatval_t;

#else
#error "libLBFGS supports single (float; LBFGS_FLOAT = 32) or double (double; LBFGS_FLOAT=64) precision only."

#endif


/**
 * Return values of lbfgs().
 * 
 *  Roughly speaking, a negative value indicates an error.
 */
enum {
  /** L-BFGS reaches convergence. */
  LBFGS_SUCCESS = 0,
  LBFGS_CONVERGENCE = 0,
  LBFGS_STOP,
  /** The initial variables already minimize the objective function. */
  LBFGS_ALREADY_MINIMIZED,
  
  /** Unknown error. */
  LBFGSERR_UNKNOWNERROR = -1024,
  /** Logic error. */
  LBFGSERR_LOGICERROR,
  /** Insufficient memory. */
  LBFGSERR_OUTOFMEMORY,
  /** The minimization process has been canceled. */
  LBFGSERR_CANCELED,
  /** Invalid number of variables specified. */
  LBFGSERR_INVALID_N,
  /** Invalid number of variables (for SSE) specified. */
  LBFGSERR_INVALID_N_SSE,
  /** The array x must be aligned to 16 (for SSE). */
  LBFGSERR_INVALID_X_SSE,
  /** Invalid parameter lbfgs_parameter_t::epsilon specified. */
  LBFGSERR_INVALID_EPSILON,
  /** Invalid parameter lbfgs_parameter_t::past specified. */
  LBFGSERR_INVALID_TESTPERIOD,
  /** Invalid parameter lbfgs_parameter_t::delta specified. */
  LBFGSERR_INVALID_DELTA,
  /** Invalid parameter lbfgs_parameter_t::linesearch specified. */
  LBFGSERR_INVALID_LINESEARCH,
  /** Invalid parameter lbfgs_parameter_t::max_step specified. */
  LBFGSERR_INVALID_MINSTEP,
  /** Invalid parameter lbfgs_parameter_t::max_step specified. */
  LBFGSERR_INVALID_MAXSTEP,
  /** Invalid parameter lbfgs_parameter_t::ftol specified. */
  LBFGSERR_INVALID_FTOL,
  /** Invalid parameter lbfgs_parameter_t::wolfe specified. */
  LBFGSERR_INVALID_WOLFE,
  /** Invalid parameter lbfgs_parameter_t::gtol specified. */
  LBFGSERR_INVALID_GTOL,
  /** Invalid parameter lbfgs_parameter_t::xtol specified. */
  LBFGSERR_INVALID_XTOL,
  /** Invalid parameter lbfgs_parameter_t::max_linesearch specified. */
  LBFGSERR_INVALID_MAXLINESEARCH,
  /** Invalid parameter lbfgs_parameter_t::orthantwise_c specified. */
  LBFGSERR_INVALID_ORTHANTWISE,
  /** Invalid parameter lbfgs_parameter_t::orthantwise_start specified. */
  LBFGSERR_INVALID_ORTHANTWISE_START,
  /** Invalid parameter lbfgs_parameter_t::orthantwise_end specified. */
  LBFGSERR_INVALID_ORTHANTWISE_END,
  /** The line-search step went out of the interval of uncertainty. */
  LBFGSERR_OUTOFINTERVAL,
  /** A logic error occurred; alternatively, the interval of uncertainty
      became too small. */
  LBFGSERR_INCORRECT_TMINMAX,
  /** A rounding error occurred; alternatively, no line-search step
      satisfies the sufficient decrease and curvature conditions. */
  LBFGSERR_ROUNDING_ERROR,
  /** The line-search step became smaller than lbfgs_parameter_t::min_step. */
  LBFGSERR_MINIMUMSTEP,
  /** The line-search step became larger than lbfgs_parameter_t::max_step. */
  LBFGSERR_MAXIMUMSTEP,
  /** The line-search routine reaches the maximum number of evaluations. */
  LBFGSERR_MAXIMUMLINESEARCH,
  /** The algorithm routine reaches the maximum number of iterations. */
  LBFGSERR_MAXIMUMITERATION,
  /** Relative width of the interval of uncertainty is at most
      lbfgs_parameter_t::xtol. */
  LBFGSERR_WIDTHTOOSMALL,
  /** A logic error (negative line-search step) occurred. */
  LBFGSERR_INVALIDPARAMETERS,
  /** The current search direction increases the objective function value. */
  LBFGSERR_INCREASEGRADIENT,
};

/**
 * Line search algorithms.
 */
enum {
  /** The default algorithm (MoreThuente method). */
  LBFGS_LINESEARCH_DEFAULT = 0,
  /** MoreThuente method proposd by More and Thuente. */
  LBFGS_LINESEARCH_MORETHUENTE = 0,
  /**
   * Backtracking method with the Armijo condition.
   *  The backtracking method finds the step length such that it satisfies
   *  the sufficient decrease (Armijo) condition,
   *    - f(x + a * d) <= f(x) + lbfgs_parameter_t::ftol * a * g(x)^T d,
   *
   *  where x is the current point, d is the current search direction, and
   *  a is the step length.
   */
  LBFGS_LINESEARCH_BACKTRACKING_ARMIJO = 1,
  /** The backtracking method with the defualt (regular Wolfe) condition. */
  LBFGS_LINESEARCH_BACKTRACKING = 2,
  /**
   * Backtracking method with regular Wolfe condition.
   *  The backtracking method finds the step length such that it satisfies
   *  both the Armijo condition (LBFGS_LINESEARCH_BACKTRACKING_ARMIJO)
   *  and the curvature condition,
   *    - g(x + a * d)^T d >= lbfgs_parameter_t::wolfe * g(x)^T d,
   *
   *  where x is the current point, d is the current search direction, and
   *  a is the step length.
   */
  LBFGS_LINESEARCH_BACKTRACKING_WOLFE = 2,
  /**
   * Backtracking method with strong Wolfe condition.
   *  The backtracking method finds the step length such that it satisfies
   *  both the Armijo condition (LBFGS_LINESEARCH_BACKTRACKING_ARMIJO)
   *  and the following condition,
   *    - |g(x + a * d)^T d| <= lbfgs_parameter_t::wolfe * |g(x)^T d|,
   *
   *  where x is the current point, d is the current search direction, and
   *  a is the step length.
   */
  LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 3,
};

/**
 * L-BFGS optimization parameters.
 *  Call lbfgs_parameter_init() function to initialize parameters to the
 *  default values.
 */
typedef struct {
  /**
   * The number of corrections to approximate the inverse hessian matrix.
   *  The L-BFGS routine stores the computation results of previous \ref m
   *  iterations to approximate the inverse hessian matrix of the current
   *  iteration. This parameter controls the size of the limited memories
   *  (corrections). The default value is \c 6. Values less than \c 3 are
   *  not recommended. Large values will result in excessive computing time.
   */
  int             m;
  
  /**
   * Epsilon for convergence test.
   *  This parameter determines the accuracy with which the solution is to
   *  be found. A minimization terminates when
   *      ||g|| < \ref epsilon * max(1, ||x||),
   *  where ||.|| denotes the Euclidean (L2) norm. The default value is
   *  \c 1e-5.
   */
  lbfgsfloatval_t epsilon;
  
  /**
   * Distance for delta-based convergence test.
   *  This parameter determines the distance, in iterations, to compute
   *  the rate of decrease of the objective function. If the value of this
   *  parameter is zero, the library does not perform the delta-based
   *  convergence test. The default value is \c 0.
   */
  int             past;
  
  /**
   * Delta for convergence test.
   *  This parameter determines the minimum rate of decrease of the
   *  objective function. The library stops iterations when the
   *  following condition is met:
   *      (f' - f) / f < \ref delta,
   *  where f' is the objective value of \ref past iterations ago, and f is
   *  the objective value of the current iteration.
   *  The default value is \c 0.
   */
  lbfgsfloatval_t delta;
  
  /**
   * The maximum number of iterations.
   *  The lbfgs() function terminates an optimization process with
   *  ::LBFGSERR_MAXIMUMITERATION status code when the iteration count
   *  exceedes this parameter. Setting this parameter to zero continues an
   *  optimization process until a convergence or error. The default value
   *  is \c 0.
   */
  int             max_iterations;
  
  /**
   * The line search algorithm.
   *  This parameter specifies a line search algorithm to be used by the
   *  L-BFGS routine.
   */
  int             linesearch;
  
  /**
   * The maximum number of trials for the line search.
   *  This parameter controls the number of function and gradients evaluations
   *  per iteration for the line search routine. The default value is \c 20.
   */
  int             max_linesearch;
  
  /**
   * The minimum step of the line search routine.
   *  The default value is \c 1e-20. This value need not be modified unless
   *  the exponents are too large for the machine being used, or unless the
   *  problem is extremely badly scaled (in which case the exponents should
   *  be increased).
   */
  lbfgsfloatval_t min_step;
  
  /**
     * The maximum step of the line search.
     *  The default value is \c 1e+20. This value need not be modified unless
     *  the exponents are too large for the machine being used, or unless the
     *  problem is extremely badly scaled (in which case the exponents should
     *  be increased).
     */
    lbfgsfloatval_t max_step;

    /**
     * A parameter to control the accuracy of the line search routine.
     *  The default value is \c 1e-4. This parameter should be greater
     *  than zero and smaller than \c 0.5.
     */
    lbfgsfloatval_t ftol;

    /**
     * A coefficient for the Wolfe condition.
     *  This parameter is valid only when the backtracking line-search
     *  algorithm is used with the Wolfe condition,
     *  ::LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE or
     *  ::LBFGS_LINESEARCH_BACKTRACKING_WOLFE .
     *  The default value is \c 0.9. This parameter should be greater
     *  the \ref ftol parameter and smaller than \c 1.0.
     */
    lbfgsfloatval_t wolfe;

    /**
     * A parameter to control the accuracy of the line search routine.
     *  The default value is \c 0.9. If the function and gradient
     *  evaluations are inexpensive with respect to the cost of the
     *  iteration (which is sometimes the case when solving very large
     *  problems) it may be advantageous to set this parameter to a small
     *  value. A typical small value is \c 0.1. This parameter shuold be
     *  greater than the \ref ftol parameter (\c 1e-4) and smaller than
     *  \c 1.0.
     */
    lbfgsfloatval_t gtol;

    /**
     * The machine precision for floating-point values.
     *  This parameter must be a positive value set by a client program to
     *  estimate the machine precision. The line search routine will terminate
     *  with the status code (::LBFGSERR_ROUNDING_ERROR) if the relative width
     *  of the interval of uncertainty is less than this parameter.
     */
    lbfgsfloatval_t xtol;

    /**
     * Coeefficient for the L1 norm of variables.
     *  This parameter should be set to zero for standard minimization
     *  problems. Setting this parameter to a positive value activates
     *  Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) method, which
     *  minimizes the objective function F(x) combined with the L1 norm |x|
     *  of the variables, {F(x) + C |x|}. This parameter is the coeefficient
     *  for the |x|, i.e., C. As the L1 norm |x| is not differentiable at
     *  zero, the library modifies function and gradient evaluations from
     *  a client program suitably; a client program thus have only to return
     *  the function value F(x) and gradients G(x) as usual. The default value
     *  is zero.
     */
    lbfgsfloatval_t orthantwise_c;

    /**
     * Start index for computing L1 norm of the variables.
     *  This parameter is valid only for OWL-QN method
     *  (i.e., \ref orthantwise_c != 0). This parameter b (0 <= b < N)
     *  specifies the index number from which the library computes the
     *  L1 norm of the variables x,
     *      |x| := |x_{b}| + |x_{b+1}| + ... + |x_{N}| .
     *  In other words, variables x_1, ..., x_{b-1} are not used for
     *  computing the L1 norm. Setting b (0 < b < N), one can protect
     *  variables, x_1, ..., x_{b-1} (e.g., a bias term of logistic
     *  regression) from being regularized. The default value is zero.
     */
    int             orthantwise_start;

    /**
     * End index for computing L1 norm of the variables.
     *  This parameter is valid only for OWL-QN method
     *  (i.e., \ref orthantwise_c != 0). This parameter e (0 < e <= N)
     *  specifies the index number at which the library stops computing the
     *  L1 norm of the variables x,
     */
    int             orthantwise_end;
} lbfgs_parameter_t;


/**
 * Callback interface to provide objective function and gradient evaluations.
 *
 *  The lbfgs() function call this function to obtain the values of objective
 *  function and its gradients when needed. A client program must implement
 *  this function to evaluate the values of the objective function and its
 *  gradients, given current values of variables.
 *  
 *  @param  instance    The user data sent for lbfgs() function by the client.
 *  @param  x           The current values of variables.
 *  @param  g           The gradient vector. The callback function must compute
 *                      the gradient values for the current variables.
 *  @param  n           The number of variables.
 *  @param  step        The current step of the line search routine.
 *  @retval lbfgsfloatval_t The value of the objective function for the current
 *                          variables.
 */
typedef lbfgsfloatval_t (*lbfgs_evaluate_t)(
    void *instance,
    const lbfgsfloatval_t *x,
    lbfgsfloatval_t *g,
    const int n,
    const lbfgsfloatval_t step
    );

/**
 * Callback interface to receive the progress of the optimization process.
 *
 *  The lbfgs() function call this function for each iteration. Implementing
 *  this function, a client program can store or display the current progress
 *  of the optimization process.
 *
 *  @param  instance    The user data sent for lbfgs() function by the client.
 *  @param  x           The current values of variables.
 *  @param  g           The current gradient values of variables.
 *  @param  fx          The current value of the objective function.
 *  @param  xnorm       The Euclidean norm of the variables.
 *  @param  gnorm       The Euclidean norm of the gradients.
 *  @param  step        The line-search step used for this iteration.
 *  @param  n           The number of variables.
 *  @param  k           The iteration count.
 *  @param  ls          The number of evaluations called for this iteration.
 *  @retval int         Zero to continue the optimization process. Returning a
 *                      non-zero value will cancel the optimization process.
 */
typedef int (*lbfgs_progress_t)(
    void *instance,
    const lbfgsfloatval_t *x,
    const lbfgsfloatval_t *g,
    const lbfgsfloatval_t fx,
    const lbfgsfloatval_t xnorm,
    const lbfgsfloatval_t gnorm,
    const lbfgsfloatval_t step,
    int n,
    int k,
    int ls
    );

#endif