Average Max Deviation for Gain. There are M Linear Loss scenario functions (every Linear Loss scenario function is defined by a Matrix of Scenarios). A new Maximum Gain Deviation scenarios function is calculated by maximizing losses over -(Linear Loss)+ (Average Linear Loss over scenarios) functions (over M functions for every scenario). Average Max Gain Deviation is calculated by averaging Maximum Gain Deviation scenarios.
Syntax
avg_max_dev_g(matrix_1,matrix_2,...,matrix_M) |
short call |
avg_max_dev_g_name(matrix_1,matrix_2,...,matrix_M) |
call with optional name |
Parameters
matrix_m is a Matrix of Scenarios:
where the header row contains names of variables (except scenario_probability, and scenario_benchmark). Other rows contain numerical data. The scenario_probability, and scenario_benchmark columns are optional.
.
Mathematical Definition
Average Max Deviation for Gain function is calculated as follows
,
where:
M = number of random Loss Functions ,
= vector of random coefficients for m-th Loss Function;
= j-th scenario of the random vector ,
is a random function with scenarios .
Every Loss Function is defined by a separate matrix of scenarios and has an equal number of scenarios J.
Probability of scenario is defined by the first matrix.
is an argument of Average Max Deviation for Gain function.
Example
See also
Average Loss, Average Gain, Average Max, Average Max for Gain, Average Max Deviations