Description Usage Arguments Value Author(s) Examples

Approximate the expected value function using fast methods.

1 2 | ```
FastExpected(grid, value, disturb, weight, r_index, Neighbour,
smooth = 1, SmoothNeighbour)
``` |

`grid` |
Matrix representing the grid, whose i-th row matrix [i,] corresponds to i-th point of the grid. The matrix [i,1] equals to 1 while the vector [i,-1] represents the system state. |

`value` |
Matrix representing the subgradient envelope of the future value function, where the intercept [i,1] and slope matrix [i,-1] describes a subgradient at grid point i. |

`disturb` |
3-dimensional array containing the disturbance matrices. Matrix [,,i] specifies the i-th disturbance matrix. |

`weight` |
Array containing the probability weights of the disturbance matrices. |

`r_index` |
Matrix representing the positions of random entries in the disturbance matrix, where entry [i,1] is the row number and [i,2] gives the column number of the i-th random entry. |

`Neighbour` |
Optional function to find the nearest neighbours. If not provided, the Neighbour function from the rflann package is used instead. |

`smooth` |
The number of nearest neighbours used to smooth the expected value functions during the Bellman recursion. |

`SmoothNeighbour` |
Optional function to find the nearest neighbours for smoothing purposes. If not provided, the Neighbour function from the rflann package is used instead. |

Matrix representing the subgradient envelope of the expected value function. Same format as the value input.

Jeremy Yee

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## Bermuda put option
grid <- as.matrix(cbind(rep(1, 91), c(seq(10, 100, length = 91))))
disturb <- array(0, dim = c(2, 2, 10))
disturb[1,1,] <- 1
disturb[2,2,] <- exp((0.06 - 0.5 * 0.2^2) * 0.02 + 0.2 * sqrt(0.02) * rnorm(10))
weight <- rep(1 / 10, 10)
control <- matrix(c(c(1, 1), c(2, 1)), nrow = 2, byrow = TRUE)
reward <- array(0, dim = c(91, 2, 2, 2, 51))
reward[grid[,2] <= 40,1,2,2,] <- 40
reward[grid[,2] <= 40,2,2,2,] <- -1
r_index <- matrix(c(2, 2), ncol = 2)
bellman <- FastBellman(grid, reward, control, disturb, weight, r_index)
expected <- FastExpected(grid, bellman$value[,,2,2], disturb, weight, r_index)
``` |

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