Maximum Group

Full Name

Brief Name

Short Description

Maximum

max_risk

Maximum of Linear Loss  scenarios.

Maximum for Gain

max_risk_g

Maximum of -(Linear Loss ) scenarios.

Maximum Deviation

max_dev

Maximum of ((Linear Loss ) - (Average over Linear Loss scenarios)) scenarios.

Maximum Deviation for Gain

max_dev_g

Maximum of (-(Linear Loss ) + (Average over Linear Loss scenarios)) scenarios.

Maximum CVaR

max_cvar_risk

There are M  Linear  Loss  scenario functions (every Linear Loss scenario function is defined by a Matrix of Scenarios). M new CVaR functions are calculated (for every Loss scenario function).  Maximum CVaR is calculated by taking Maximum over M CVaR functions.

Maximum CVaR for Gain

max_cvar_risk_g

There are M Linear Loss scenario functions (every Linear Loss scenario function is defined by a Matrix of Scenarios). M new CVaR for Gain functions are calculated (for every -(Loss) scenario function).  Maximum CVaR for Gain is calculated by taking Maximum over M CVaR for Gain  functions (based on -(Loss) scenarios).

Maximum CVaR Deviation

max_cvar_dev

There are M Linear Loss scenario functions (every Linear Loss scenario function is defined by a Matrix of Scenarios). M new CVaR Deviation functions are calculated (for every Loss scenario function).  Maximum CVaR Deviation is calculated by taking Maximum over M CVaR Deviation  functions.

Maximum CVaR Deviation for Gain

max_cvar_dev_g

There are M Linear Loss scenario functions (every Linear Loss scenario function is defined by a Matrix of Scenarios). M new CVaR Deviation for Gain functions are calculated (for every -(Loss) scenario function).  Maximum CVaR Deviation for Gain is calculated by taking Maximum over M CVaR Deviation for Gain  functions (based on -(Loss) scenarios).

Maximum VaR

max_var_risk

There are M  Linear Loss scenario functions (every Linear Loss  scenario function is defined by a Matrix of Scenarios). M new VaR functions are calculated (for every Loss scenario function).  Maximum VaR is calculated by taking Maximum over M VaR functions.

Maximum VaR for Gain

max_var_risk_g

There are M  Linear Loss scenario functions (every Linear Loss  scenario function is defined by a Matrix of Scenarios). M new VaR for Gain functions are calculated (for every -(Loss) scenario function).  Maximum VaR for Gain is calculated by taking Maximum over M VaR for Gain functions.

Maximum VaR Deviation

max_var_dev

There are M  Linear  Loss  scenario functions (every Linear Loss scenario function is defined by a Matrix of Scenarios). M new VaR Deviation  functions are calculated (for every Loss scenario function).  Maximum VaR Deviation is calculated by taking Maximum over M VaR Deviation  functions.

Maximum VaR Deviation for Gain

max_var_dev_g

There are M  Linear  Loss  scenario functions (every Linear Loss scenario function is defined by a Matrix of Scenarios). M new VaR Deviation for Gain functions are calculated (for every -(Loss) scenario function).  Maximum VaR Deviation for Gain is calculated by taking Maximum over M VaR Deviation for Gain  functions (based on -(Loss) scenarios).

Maximum Recourse

max_risk(recourse(.))

Maximum over Recourse scenarios. Recourse scenarios are obtained by solving LP at the second stage of two-stage stochastic programming problem for every scenario.

Maximum for Gain Recourse

max_risk_g(recourse(.))

Maximum over -(Recourse) scenarios. Recourse scenarios are obtained by solving LP at the second stage of two-stage stochastic programming problem for every scenario.

Maximum Deviation Recourse

max_dev(recourse(.))

Maximum over (Recourse)-(Expected Recourse) scenarios. Recourse scenarios are obtained by solving LP at the second stage of two-stage stochastic programming problem for every scenario.

Maximum Deviation for Gain Recourse

max_dev_g(recourse(.))

Maximum over -(Recourse)+(Expected Recourse) scenarios. Recourse scenarios are obtained by solving LP at the second stage of two-stage stochastic programming problem for every scenario.