rpsg_verify: Verify PSG Problem
In PSGExpress: Portfolio Safeguard: Optimization, Statistics and Risk Management

Description Usage Arguments Value Note Author(s) References See Also Examples

To use PSG solvers it is necessary to prepare problem in General (Text) format. There are three main objects of PSG:

Problem Statement. It is a character with description of optimization problem. This object must be created according to the rules of PSG General format.
Problem statement for optimization problems with one constraint:

(minimize|maximize)
[<coef1>*]<PSG function obj 1>
.........
[<coefK>*]<PSG function obj K>
Constraint:(<=|>=|==) <bound>
[<coef1>*]<PSG function constr 1>
.........
[<coefK>*]<PSG function constr K>
Box: (<=|>=|==) <bound>
PSG Data Objects. Data for optimization problem in a specific format: PSG Matrix, PSG PMatrix, PSG Point, and PSG Vector.
PSG Solution. The results of optimization problem are stored in solution report (character) and PSG Data Objects (PSG Matrix, PSG Point, ect).

1 2	rpsg_verify(problem_list, rho = parent.frame(), allowExt = TRUE, rpsg_suppress.Messages = FALSE)

`problem_list`	list with data for optimization problem. List members: `problem_list$problem_statement` character with PSG Problem Statement; `problem_list$matrix_<name>` matrix with names of columns that correspond to names of optimization variables. Name of this list member (matrix_<name>) must correspond to the name of PSG Matrix in Problem Statement. Matrix may include two optional collumns: scenario_benchmark and scenario_probability; `problem_list$pmatrix_<name>` sparse matrix with names of columns that correspond to names of optimization variables. Name of this list member (pmatrix_<name>) must correspond to the name of PSG PMatrix in Problem Statement. PMatrix may include two optional collumns: scenario_benchmark and scenario_probability; `problem_list$point_<name>` vector with names of members that correspond to names of optimization variables. Name of this list member (point_<name>) must correspond to the name of PSG Point in Problem Statement; `problem_list$vector_<name>` vector with data. Name of this list member (vector_<name>) must correspond to the name of PSG Vector in Problem Statement.
`rho`	optional parameter for setting frame. Default is rho = parent.frame().
`allowExt`	optional parameter to specify if a solver can use variables from data frame defined in rho (by the default allowExt = TRUE) or not (allowExt = FALSE).
`rpsg_suppress.Messages`	optional parameter specifying if messages, that may appear when you run this function, should be suppressed (rpsg_suppress.Messages = TRUE) or not (by the default: rpsg_suppress.Messages = FALSE).

logical variable = problem is correct or not.

rpsg_solver supports two cases of input data:
1. all PSG data objects are saved in input list,
2. all PSG data objects are saved in Global Environment.
In both cases input list must include PSG Problem Statement (problem_list$problem_statement) and names of PSG data objects (names of list members or variables) should be used in Problem Statement.

Stan Uryasev [aut, cre, cph], Grigoriy Zrazhevsky [aut], Viktor Kuzmenko [aut], Alex Zrazhevsky [aut]

Maintainer: Stan Uryasev <stan.uryasev@aorda.com>

American Optimal Decisions
Portfolio Safeguard Help

rpsg_solver

#Problem of CVaR minimization with constraint on the mean profit:
#Find x = (x1,x2,x3,x4) minimizing
#risk(x) = CVaR(0.95,x)
#subject to
#Average Gain(x)>4.5
#x1+x2+x3+x4 = 1
#x1>=0, x2>=0, x3>=0, x4>=0

matrix_scenarios <- matrix(c(1,4,8,3, 7,5,4,6, 2,8,1,0,0,3,4,9),nrow=4, byrow=TRUE)
colnames(matrix_scenarios) <- colnames(matrix_scenarios,do.NULL = FALSE, prefix = "x")
scenario_benchmark <- c(0.2, 0.11, 0.6, 0.1)
matrix_scenarios <- cbind(matrix_scenarios,scenario_benchmark)
matrix_budget <- matrix(c(1, 1, 1, 1),nrow=1)
colnames(matrix_budget) <- colnames(matrix_budget,do.NULL = FALSE, prefix = "x")
point_lowerbounds <- c(0, 0, 0, 0)
names(point_lowerbounds) <- rownames(point_lowerbounds,do.NULL = FALSE, prefix = "x")

problem_list <- list()

#Problem Statement
problem_list$problem_statement <- sprintf(
 "minimize
 cvar_risk(0.95,matrix_scenarios)
Constraint: >= 4.5
 avg_g(matrix_scenarios)
Constraint: == 1
 linear(matrix_budget)
Box: >= point_lowerbounds")

# PSG Matrix:
problem_list$matrix_scenarios <- matrix_scenarios

# PSG Matrix:
problem_list$matrix_budget <- matrix_budget

# PSG Point:
problem_list$point_lowerbounds <- point_lowerbounds

# Solve optimization problem
output <- rpsg_verify(problem_list)