// Copyright 2014 Google Inc. All Rights Reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
// Author: nevena@google.com (Nevena Lazic)
//
// An implementation of:
// I. Mukherjee, K. Canini, R. Frongillo, and Y. Singer, "Parallel Boosting with
// Momentum", ECML PKDD 2013.
// Variable names follow the notation in the paper.

#pragma once

#include "lossmin/eigen-types.h"
#include "lossmin/minimizers/gradient-evaluator.h"
#include "lossmin/minimizers/loss-minimizer.h"

namespace lossmin {

class GradientEvaluator;

class ParallelBoostingWithMomentum : public LossMinimizer {
 public:
  ParallelBoostingWithMomentum(
      float l1, float l2, const GradientEvaluator &gradient_evaluator)
      : LossMinimizer(l1, l2, gradient_evaluator) {
    Setup();
  }

  // Sets learning rates and other parameters.
  void Setup() override;

 private:
  // Updates 'weights' and the quadratic approximation function phi(w), such
  // that at iteration k, loss(weights_k) <= min_w phi_k(w).
  //     y = (1 - alpha) * weights + alpha * phi_center
  //     grad_y = loss_grad(y) + l2 * y
  //     weights[j] = weights[j] - grad_y[j] / learning_rates[j]
  //     weights[j] =
  //         sign(weights[j]) * max(0, weights[j], l1 / learning_rates[j])
  void EpochUpdate(Weights *weights, int epoch,
                   bool check_convergence) override;

  // Per-coordinate learning rates.
  VectorXf learning_rates_;

  // Center of the approximating quadratic function phi.
  VectorXf phi_center_;

  // Parameter for updating the approximation function phi. At each iteration,
  // 'alpha_' is updated to the solution of the quadratic equation
  //     alpha_^2 = beta_ * (1.0 - alpha_)
  float alpha_;

  // Parameter used to update alpha, defined as
  //     beta_{epoch} = \prod_{i=1}^{epoch} (1 - alpha_i).
  float beta_;
};

}  // namespace lossmin