| Type: | Package |
| Title: | 'MatLab'-Style Modeling of Optimization Problems |
| Version: | 1.0-2 |
| Date: | 2018-08-17 |
| Maintainer: | Ronald Hochreiter <ron@hochreiter.net> |
| Description: | 'MatLab'-Style Modeling of Optimization Problems with 'R'. This package provides a set of convenience functions to transform a 'MatLab'-style optimization modeling structure to its 'ROI' equivalent. |
| Depends: | R (≥ 3.4), ROI, ROI.plugin.glpk, ROI.plugin.quadprog |
| License: | MIT + file LICENSE |
| URL: | http://www.finance-r.com/ |
| RoxygenNote: | 6.1.0 |
| NeedsCompilation: | no |
| Packaged: | 2018-08-18 15:24:34 UTC; ron |
| Author: | Ronald Hochreiter [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2018-08-18 16:00:03 UTC |
MatLab(R)-style Optimization Modeling in R using ROI
Description
'MatLab'-Style Modeling of Optimization Problems with 'R'. This package provides a set of convenience functions to transform a 'MatLab'-style optimization modeling structure to its 'ROI' equivalent.
Author(s)
Ronald Hochreiter, ron@hochreiter.net
References
See Also
Useful links:
MatLab(R)-style Mixed Integer Linear Programming in R using ROI
Description
intlinprog provides a simple interface to ROI using the optimization
model specification of MatLab(R)
minimize in x: f'*x subject to A*x <= b Aeq*x == beq x >= lb x <= ub
Usage
intlinprog(f, intcon = NULL, A = NULL, b = NULL, Aeq = NULL,
beq = NULL, lb = NULL, ub = NULL, x0 = NULL, options = NULL)
Arguments
f |
Linear term (vector) of the objective function |
intcon |
Vector of which variables are integer |
A |
Inequality constraints (left-hand side) |
b |
Inequality constraints (right-hand side) |
Aeq |
Equality constraints (left-hand side) |
beq |
Equality constraints (right-hand side) |
lb |
Lower bound |
ub |
Upper bound |
x0 |
Initial solution |
options |
Additional optimization parameters |
Value
The solution vector in x as well as the objective value
in fval.
Author(s)
Ronald Hochreiter, ron@hochreiter.net
Examples
# minimize 8x1 + x2
# subject to
# x1 + 2x2 >= -14
# -4x1 - 1x2 <= -33
# 2x1 + x2 <= 20
# x1, x2 integer
f <- c(8, 1)
A <- matrix(c(-1, -2, -4, -1, 2, 1), nrow=3, byrow=TRUE)
b <- c(14, -33, 20)
sol <- intlinprog(f, c(1, 2), A, b)
sol <- intlinprog(f, NULL, A, b)
sol$x
MatLab(R)-style Linear Programming in R using ROI
Description
linprog provides a simple interface to ROI using the optimization
model specification of MatLab(R)
minimize in x: f'*x subject to: A*x <= b subject to: Aeq*x == beq x >= lb x <= ub
Usage
linprog(f, A = NULL, b = NULL, Aeq = NULL, beq = NULL, lb = NULL,
ub = NULL, x0 = NULL, options = NULL)
Arguments
f |
Linear term (vector) of the objective function |
A |
Inequality constraints (left-hand side) |
b |
Inequality constraints (right-hand side) |
Aeq |
Equality constraints (left-hand side) |
beq |
Equality constraints (right-hand side) |
lb |
Lower bound |
ub |
Upper bound |
x0 |
Initial solution |
options |
Additional optimization parameters |
Value
The solution vector in x as well as the objective value
in fval.
Author(s)
Ronald Hochreiter, ron@hochreiter.net
Examples
# maximize: 2x1 + x2
# subject to:
# x1 + x2 <= 5
# x1 <= 3
# x1 >= 0, x2 >= 0
f <- c(2, 1)
A <- matrix(c(1, 1, 1, 0), nrow=2, byrow=TRUE)
b <- c(5, 3)
sol <- linprog(-f, A, b)
sol$x
MatLab(R)-style Quadratic Programming in R using ROI
Description
quadprog provides a simple interface to ROI using the optimization
model specification of MatLab(R)
minimize in x: f'*x + 0.5*x'*H*x subject to: A*x <= b Aeq*x == beq x >= lb x <= ub
Usage
quadprog(H, f, A = NULL, b = NULL, Aeq = NULL, beq = NULL,
lb = NULL, ub = NULL, x0 = NULL, options = NULL)
Arguments
H |
Quadratic term (matrix) of the objective function |
f |
Linear term (vector) of the objective function |
A |
Inequality constraints (left-hand side) |
b |
Inequality constraints (right-hand side) |
Aeq |
Equality constraints (left-hand side) |
beq |
Equality constraints (right-hand side) |
lb |
Lower bound |
ub |
Upper bound |
x0 |
Initial solution |
options |
Additional optimization parameters |
Value
The solution vector in x as well as the objective value
in fval.
Author(s)
Ronald Hochreiter, ron@hochreiter.net
Examples
# Covariance matrix of four stocks (weekly returns from 2011):
#
# AAPL IBM MSFT ORCL
# AAPL 0.0014708114 0.0006940036 0.0006720841 0.0008276391
# IBM 0.0006940036 0.0009643581 0.0006239411 0.0011266429
# MSFT 0.0006720841 0.0006239411 0.0009387707 0.0008728736
# ORCL 0.0008276391 0.0011266429 0.0008728736 0.0021489512
covariance = matrix(c(0.0014708114, 0.0006940036, 0.0006720841, 0.0008276391,
0.0006940036, 0.0009643581, 0.0006239411, 0.0011266429,
0.0006720841, 0.0006239411, 0.0009387707, 0.0008728736,
0.0008276391, 0.0011266429, 0.0008728736, 0.0021489512),
nrow=4, byrow=TRUE)
assets <- dim(covariance)[1]
H <- covariance
f <- rep(0, assets)
Aeq <- rep(1, assets)
beq <- 1
lb <- rep(0, assets)
ub <- rep(1, assets)
solution <- quadprog(H, f, NULL, NULL, Aeq, beq, lb, ub)
portfolio <- solution$x
print(portfolio)