rpsg_getsolution: Convert character PSG solution report into list witn numeric...
In PSGExpress: Portfolio Safeguard: Optimization, Statistics and Risk Management

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

Standard output of rpsg_solver contains information in character string problem.res$output. rpsg_getsolution function exctracts the main information from character string and creates list with numeric data.

1	rpsg_getsolution(problem.res)

problem.res

list with solution results of the optimization problem obtained by rpsg_solver

problem.res$problem_name: name (problem_<name>) of the optimization problem. By default: problem_1;
problem.res$solution_status: status of the solution of the optimization problem: optimal | feasible | infeasible | unbounded | calculated (for calculation problems);
problem.res$problem_statement: Problem Statement of the solved problem;
problem.res$output: solution report = character with main information about optimization results;
problem.res$point_constraints_problem_<name>: optimal values of left hand sides of constraints in the optimization problem;
problem.res$point_slack_constraints_problem_<name>: slacks value for constraints of the optimization problem;
problem.res$point_dual_constraints_problem_<name>: dual constraints values;
problem.res$point_problem_<name>: optimal point for solved optimization problem;
problem.res$log: information about solving process.

output.list = list with solution results. List members:

output.list$status: status of the solution of the optimization problem: optimal | feasible | infeasible | unbounded | calculated (for calculation problems);
output.list$loading.time: data loading time;
output.list$preprocessing.time: preprocessing time;
output.list$solving.time: solving time;
output.list$objective: optimal value of objective function;
output.list$gap: difference between objective value in obtained point and Lower estimate of optimal value;
output.list$function.value: optimal values of PSG functions defined in the problem;
output.list$point_constraints_problem_<name>: optimal values of left hand sides of constraints in the optimization problem;
output.list$point_slack_constraints_problem_<name>: slacks value for constraints of the optimization problem;
output.list$point_dual_constraints_problem_<name>: dual constraints values;
output.list$point_problem_<name>: optimal point for solved optimization problem;

Lists with solution results, problem.res and output.list, may include output matrices, vectors and points not mentioned above. It is due to the specific characteristics of some PSG functions used in optimization problem.

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

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

American Optimal Decisions
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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
Solver: CAR")

# 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
output1 <- rpsg_solver(problem_list)

output2 <- rpsg_getsolution(output1)

print(output2)