Transcript (infeasible).

Advanced GAMS
1.
Good Modeling Practices
2.
Fixing Misbehaving Models
3.
Linking GAMS
4.
Advanced GAMS Syntax
1 Good Modeling Practices
 Document
 Descriptive
Names
 Sensible
Structure
 Efficient
Programming
Documentation

Use “*” comments

Symbol descriptions & units at declaration

$ontext $offtext

$oneolcom, $eolcom, $oninlinecom, …

Put all data in named parameters

Keep original data files

Track gams program versions
Naming

Choose Descriptive Names up to 64 Characters

Avoid Special Characters

Avoid set elements which look like numbers

Use …_var, …_equ, …_data


Use desired capitalization on 1st occurrence, be
aware of unique element ordering
Name files which need to be run sequentially 1_...,
2_..., 3_...
Structure
 Number
and size of files
 Number
and organization of
directories
 Arrangement
of sets, data, model,
and base report
 Scenarios,
Scenario Report
Efficient GAMS usage
 Use
indexes, keep order index
consistent
 Automate
put)
data processing (gdx, awk,
 Experiment
with small models
Efficient GAMS usage
 Taylor
output
 Check
aggregate solutions first
 Check
speed and memory
 Avoid
irrelevant computations - use
conditions
Efficiency and Errors
 Avoid
copies of the same piece of
code to facilitate model development
and to reduce errors from incomplete
updating
– use indexes
– may introduce accounting variables to
reduce code copies in report statements
2 Fixing Misbehaving Models
 Unboundedness
 Infeasibility
 Bad
solution
 Scaling,
Memory, Speed
Problems
Using GAMSCHK
 Option
LP=GAMSCHK;
 Option MIP=GAMSCHK;
 Option NLP=GAMSCHK;
Place option before solve statement
Works best for large LP models where
variables and equations have many
indexes
GAMSCHK
 Use
on single solve statements (not
in a scenario loop)
 Start
 For
with small model versions
larger models consider variable
and equation selections
Tayloring GAMSCHK
 Create
<filename>.gck, where
filename is the name of the file that
is executed
 Create gamschk.opt and use
<modelname>.optfile = 1;
 Create/modify <defaultsolver>.opt
Content of <filename>.gck
 Major
keywords:
– Analysis
– Postopt
– Displaycr
– Blockpic
 Variable/Equation
selection possible
under each keyword
 … see solver manual or McCarl user
guide for more exampels
Unboundedness
 Place
artificial bound on objective
or other variables
 Use
GAMSCHK nonopt
 Set
filter in gamschk.opt
Infeasibility
 Need
artificial variables
 Use
BLOCKPIC to find candidate
equations for artificial variables
 GAMSCHK
 GAMSCHK
analysis
postopt on non-zero
artificial variables
Equation Candidates for Artificial
Variables using Blockpic Output
| V V V V |
R
| a a a a |
H
| 1 2 3 4 |
S
--------------------Equ_1 | + + + - | E 0
Equ_2 | +
| L Equ_3 | - m m
| L 0
Equ_4 |
+
| G +
--------------------Typ | + + + u
Assume that all
variables are
zero and then
check which
equations would
be violated
(infeasible).
Thus, look for “L
–”, “G +”, “E -“,
or “E +”
Artificial Variables, 1
 Declare
artificial variables as
nonnegative variables
 Place a high penalty for artificial
variables in the objective function
– High negative values in max problems
– High positive values in min problems
 Use
each artificial variable only in one
constraint
Artificial Variables, 2
 For
each =L= equation with a negative
r.h.s. value add the artificial variable to
the l.h.s with a coefficient of -1.
 For each =G= equation with a positive
r.h.s. value add the artificial variable to
the l.h.s with a coefficient of 1.
 For each =E= equation with a non-zero
r.h.s. value add two artificial variables
to the l.h.s one with a coefficient of 1
and one with a coefficient of -1.
Bad solution
 GAMSCHK
analysis routine
 GAMSCHK
postopt routine
 Trace
from symptom to cause(s)
Tracing unrealistic solutions
 No
need to guess or remember all
changes from the last working model
 Decompose
 Get
solution
rid of generic errors using
GAMSCHK ANALYSIS routine
Tracing unrealistic solutions
 Alternate
between variables and
equations in GAMSCHK
 Alternate
between individual
GAMSCHK output and aggregated
model structure
 Use
display statements (don‘t look
at original data)
Fixing Analysis Errors
 First
decide between 2 general cases
 A)
Variable/Equation is present but
shouldn‘t be present
 B)
Variable/Equation is not present
but should be present
Case A) Variable/Equation is
present but shouldn‘t be present
Solution (99%):
 Impose
conditions
Case B) Variable/Equation is not
present but should be present
Solution:
 Check
conditions and data
 Display
data right before solve
statement
Postopt Output

Output for Variables
–
–
–
–

Aij coefficients
Ui shadow prices
Aij * Ui
True reduced costs
Output for Equations
–
–
–
–
Aij coefficients
Xj variable levels
Aij*Xj
Shadow price
Conditions
 Rule
of thumb: Every variable block
should have the same conditions in
each equation block