Solver Square Comparison

Solver Square Comparison: Considers all models.

Date / Time: 09/29/08 16:20:27

Solver comparison utility.

Compares all solver return outcomes (for example optimal, locally optimal, infeasible, unbounded, fail) of one solver with all return outcomes of another solver. Interrupt denotes resource or iteration limit has been reached. Solver LINDOGLOBAL is represented on the left (rows) and solver ALPHAECP on top (columns). See the solver return definitions for return codes.

Models having trace data only in one trace file are listed in the "no data" column of the other.

Tracefile 1 :	`LINDOGLOBAL-1.trc.convex`
Tracefile 2 :	`ALPHAECP-1.trc.convex`
Solvers used :	`LINDOGLOBAL`
	`ALPHAECP`
Modeltype(s)	`MINLP`

	ALPHAECP: optimal	ALPHAECP: feasible	ALPHAECP: infeasible	ALPHAECP: unbounded	ALPHAECP: fail	ALPHAECP: no data	total LINDOGLOBAL
LINDOGLOBAL: optimal	-	`23`	-	-	-	-	`23`
LINDOGLOBAL: feasible	-	`13`	-	-	`1`	-	`14`
LINDOGLOBAL: infeasible	-	-	-	-	-	-	`-`
LINDOGLOBAL: unbounded	-	-	-	-	-	-	`-`
LINDOGLOBAL: fail	-	-	-	-	`1`	-	`1`
LINDOGLOBAL: no data	-	-	-	-	-	-	`-`
total ALPHAECP	`-`	`36`	`-`	`-`	`2`	`-`	`38`

Solver return definitions:

Outcome	Model Status	Solver Status
optimal	1 or 15	1
locally optimal	2	any
feasible	8 or 16	1 or 2 or 3 or 4 or 5
infeasible	4 or 5 or 10 or 19	1
unbounded	3 or 18	1
fail	all other	all other

Solver Resource Times

Models for each solver pair outcome. Listed are the solver resource times TIME(.) in seconds, as well as the ratio RATIO(./.) of resource times for the two solvers if both solved optimally.
Also listed are the objective values OBJ(.) using both solvers. The better solution found is listed in boldface. A solution is considered better, if the relative objective function difference is greater than 1.00E-05. If both solutions are less than 1e-1, we use the absolute difference.
Solver resource time ratios for a particular model are listed only if one solver has resource greater than 5.00E-02.

LINDOGLOBAL: optimal -- ALPHAECP: feasible Back to top

Modelname	Time (LINDOGLOBAL)	Time (ALPHAECP)	Ratio (LINDOGLOBAL/ALPHAECP)	Obj (LINDOGLOBAL)	Obj (ALPHAECP)
`alan`	`0.0250`	`0.0700`	`0.357`	`2.92500000`	`2.92500000`
`batch`	`2.2470`	`4.1000`	`0.548`	`285506.50824681`	`285506.50824403`
`ex1223`	`0.0970`	`0.1600`	`0.606`	`4.57958240`	`4.57958240`
`ex1223a`	`0.0220`	`0.0800`	`0.275`	`4.57958240`	`4.57958240`
`ex1223b`	`0.0730`	`0.1300`	`0.562`	`4.57958240`	`4.57958240`
`fac3`	`53.8090`	`0.5700`	`94.402`	`31982309.84800090`	`31982309.84799990`
`gbd`	`0.0090`	`0.0100`	`---`	`2.20000000`	`2.20000000`
`m3`	`1.6960`	`0.2500`	`6.784`	`37.79999992`	`37.80000000`
`nvs03`	`0.0270`	`0.0100`	`---`	`16.00000000`	`16.00000000`
`risk2bpb`	`2.2250`	`1.1000`	`2.023`	`-55.87613939`	`-55.87613940`
`st_e14`	`0.1490`	`0.1100`	`1.355`	`4.57958240`	`4.57958240`
`st_miqp1`	`0.0310`	`0.0000`	`---`	`281.00000000`	`281.00000000`
`st_miqp2`	`0.0440`	`0.0400`	`---`	`2.00000000`	`2.00000000`
`st_miqp3`	`0.0080`	`0.0100`	`---`	`-6.00000000`	`-6.00000000`
`st_miqp5`	`0.0120`	`0.0400`	`---`	`-333.88888889`	`-333.88888889`
`st_test5`	`0.0290`	`0.0500`	`0.580`	`-110.00000000`	`-110.00000000`
`st_test6`	`0.0370`	`0.0200`	`---`	`471.00000000`	`471.00000000`
`st_test8`	`0.0290`	`0.0100`	`---`	`-29605.00000000`	`-29605.00000000`
`synthes1`	`0.0440`	`0.0700`	`0.629`	`6.00975902`	`6.00975891`
`synthes2`	`0.5900`	`0.2700`	`2.185`	`73.03531253`	`73.03531252`
`synthes3`	`4.3810`	`0.8500`	`5.154`	`68.00974052`	`68.00974052`
`st_testgr3`	`11.1490`	`0.0600`	`185.817`	`-20.59000000`	`-20.59000000`
`st_testph4`	`0.0110`	`0.0100`	`---`	`-80.50000000`	`-80.50000000`

LINDOGLOBAL: feasible -- ALPHAECP: feasible Back to top

Modelname	Time (LINDOGLOBAL)	Time (ALPHAECP)	Ratio (LINDOGLOBAL/ALPHAECP)	Obj (LINDOGLOBAL)	Obj (ALPHAECP)
`du-opt5`	`2047.5980`	`12.8720`	`159.074`	`8.07365758`	`8.07365758`
`du-opt`	`270.9360`	`3.0900`	`87.682`	`3.55634006`	`3.55923932`
`fac1`	`0.3610`	`0.1000`	`3.610`	`160912612.35017100`	`160912612.35016900`
`fo7`	`3600.3070`	`356.4500`	`10.100`	`29.54373631`	`20.72982507`
`m6`	`495.2620`	`3.5800`	`138.341`	`82.25687760`	`82.25687690`
`m7`	`3600.4280`	`11.6400`	`309.315`	`106.75687693`	`106.75687690`
`meanvarx`	`0.0820`	`0.1200`	`0.683`	`14.36923211`	`14.36923211`
`nvs10`	`0.0170`	`0.0400`	`---`	`-310.80000000`	`-310.80000000`
`o7`	`3600.4330`	`3456.2900`	`1.042`	`151.30711666`	`131.65313814`
`st_miqp4`	`0.0060`	`0.0000`	`---`	`-4574.00000000`	`-4574.00000000`
`tls6`	`3600.8170`	`3600.9000`	`1.000`	`20.70000003`	`38.10000000`
`st_testgr1`	`1.4540`	`0.0800`	`18.175`	`-12.81160000`	`-12.81160000`
`stockcycle`	`310.0830`	`3600.2400`	`0.086`	`120532.62166667`	`123665.84333333`

LINDOGLOBAL: feasible -- ALPHAECP: fail Back to top

Modelname	Time (LINDOGLOBAL)	Time (ALPHAECP)	Ratio (LINDOGLOBAL/ALPHAECP)	Obj (LINDOGLOBAL)	Status (ALPHAECP)
`tls7`	`3602.9820`	`3600.5500`	---	`22.50000000`	`mstat(14) sstat( 3)`

LINDOGLOBAL: fail -- ALPHAECP: fail Back to top

Modelname	Time (LINDOGLOBAL)	Time (ALPHAECP)	Ratio (LINDOGLOBAL/ALPHAECP)	Status (LINDOGLOBAL)	Status (ALPHAECP)
`tls12`	`3607.4790`	`3600.7300`	---	`mstat(14) sstat( 3)`	`mstat(14) sstat( 3)`

LINDOGLOBAL: optimal: Back to top

Modelname	Time (LINDOGLOBAL)	Obj (LINDOGLOBAL)
`alan`	`0.0250`	`2.92500000`
`batch`	`2.2470`	`285506.50824681`
`ex1223`	`0.0970`	`4.57958240`
`ex1223a`	`0.0220`	`4.57958240`
`ex1223b`	`0.0730`	`4.57958240`
`fac3`	`53.8090`	`31982309.84800090`
`gbd`	`0.0090`	`2.20000000`
`m3`	`1.6960`	`37.79999992`
`nvs03`	`0.0270`	`16.00000000`
`risk2bpb`	`2.2250`	`-55.87613939`
`st_e14`	`0.1490`	`4.57958240`
`st_miqp1`	`0.0310`	`281.00000000`
`st_miqp2`	`0.0440`	`2.00000000`
`st_miqp3`	`0.0080`	`-6.00000000`
`st_miqp5`	`0.0120`	`-333.88888889`
`st_test5`	`0.0290`	`-110.00000000`
`st_test6`	`0.0370`	`471.00000000`
`st_test8`	`0.0290`	`-29605.00000000`
`synthes1`	`0.0440`	`6.00975902`
`synthes2`	`0.5900`	`73.03531253`
`synthes3`	`4.3810`	`68.00974052`
`st_testgr3`	`11.1490`	`-20.59000000`
`st_testph4`	`0.0110`	`-80.50000000`

LINDOGLOBAL: feasible: Back to top

Modelname	Time (LINDOGLOBAL)	Obj (LINDOGLOBAL)
`du-opt5`	`2047.5980`	`8.07365758`
`du-opt`	`270.9360`	`3.55634006`
`fac1`	`0.3610`	`160912612.35017100`
`fo7`	`3600.3070`	`29.54373631`
`m6`	`495.2620`	`82.25687760`
`m7`	`3600.4280`	`106.75687693`
`meanvarx`	`0.0820`	`14.36923211`
`nvs10`	`0.0170`	`-310.80000000`
`o7`	`3600.4330`	`151.30711666`
`st_miqp4`	`0.0060`	`-4574.00000000`
`tls6`	`3600.8170`	`20.70000003`
`tls7`	`3602.9820`	`22.50000000`
`st_testgr1`	`1.4540`	`-12.81160000`
`stockcycle`	`310.0830`	`120532.62166667`

LINDOGLOBAL: fail: Back to top

Modelname	Time (LINDOGLOBAL)	Status (LINDOGLOBAL)
`tls12`	`--`	`mstat(14) sstat( 3)`

ALPHAECP: feasible: Back to top

Modelname	Time (ALPHAECP)	Obj (ALPHAECP)
`alan`	`0.0700`	`2.92500000`
`batch`	`4.1000`	`285506.50824403`
`du-opt5`	`12.8720`	`8.07365758`
`du-opt`	`3.0900`	`3.55923932`
`ex1223`	`0.1600`	`4.57958240`
`ex1223a`	`0.0800`	`4.57958240`
`ex1223b`	`0.1300`	`4.57958240`
`fac1`	`0.1000`	`160912612.35016900`
`fac3`	`0.5700`	`31982309.84799990`
`fo7`	`356.4500`	`20.72982507`
`gbd`	`0.0100`	`2.20000000`
`m3`	`0.2500`	`37.80000000`
`m6`	`3.5800`	`82.25687690`
`m7`	`11.6400`	`106.75687690`
`meanvarx`	`0.1200`	`14.36923211`
`nvs03`	`0.0100`	`16.00000000`
`nvs10`	`0.0400`	`-310.80000000`
`o7`	`3456.2900`	`131.65313814`
`risk2bpb`	`1.1000`	`-55.87613940`
`st_e14`	`0.1100`	`4.57958240`
`st_miqp1`	`0.0000`	`281.00000000`
`st_miqp2`	`0.0400`	`2.00000000`
`st_miqp3`	`0.0100`	`-6.00000000`
`st_miqp4`	`0.0000`	`-4574.00000000`
`st_miqp5`	`0.0400`	`-333.88888889`
`st_test5`	`0.0500`	`-110.00000000`
`st_test6`	`0.0200`	`471.00000000`
`st_test8`	`0.0100`	`-29605.00000000`
`synthes1`	`0.0700`	`6.00975891`
`synthes2`	`0.2700`	`73.03531252`
`synthes3`	`0.8500`	`68.00974052`
`tls6`	`3600.9000`	`38.10000000`
`st_testgr1`	`0.0800`	`-12.81160000`
`st_testgr3`	`0.0600`	`-20.59000000`
`st_testph4`	`0.0100`	`-80.50000000`
`stockcycle`	`3600.2400`	`123665.84333333`

ALPHAECP: fail: Back to top

Modelname	Time (ALPHAECP)	Status (ALPHAECP)
`tls12`	`--`	`mstat(14) sstat( 3)`
`tls7`	`--`	`mstat(14) sstat( 3)`

LINDOGLOBAL: all -- ALPHAECP: all : Back to top

Rows with light red background in the model name mark inconsistencies, caused by negative gaps or objective values of one solver that are better than the objestive estimate of the other solver.
A "(max)" behind a model names indicates that this model is a maximization model.

A model is counted as solved if the gap reported by the solver is at most 0.015. The gap is in this case displayed with a green background

For time and timeratios, a shifted geometric mean is used, where unsolved instances are counted with twice the reslim (3600.00 seconds). The shift is 10s for time and 0.0001 for timeratios.

Modelname	Time (LINDOGLOBAL)	Time (ALPHAECP)	Ratio (LINDOGLOBAL/ALPHAECP)	Obj (LINDOGLOBAL)	Obj (ALPHAECP)	ObjEst (LINDOGLOBAL)	ObjEst (ALPHAECP)	Gap (LINDOGLOBAL)	Gap (ALPHAECP)
`alan`	`0.0250`	`0.0700`	`0.357`	`2.92500000`	`2.92500000`	`2.92499999`	`+INF`	`0.0000`	`NA`
`batch`	`2.2470`	`4.1000`	`0.548`	`285506.50824681`	`285506.50824403`	`285506.50824681`	`+INF`	`0.0000`	`NA`
`du-opt5`	`2047.5980`	`12.8720`	`159.074`	`8.07365758`	`8.07365758`	`8.05410378`	`+INF`	`0.0024`	`NA`
`du-opt`	`270.9360`	`3.0900`	`87.682`	`3.55634006`	`3.55923932`	`3.53949327`	`+INF`	`0.0048`	`NA`
`ex1223`	`0.0970`	`0.1600`	`0.606`	`4.57958240`	`4.57958240`	`4.57958240`	`+INF`	`0.0000`	`NA`
`ex1223a`	`0.0220`	`0.0800`	`0.275`	`4.57958240`	`4.57958240`	`4.57958240`	`+INF`	`0.0000`	`NA`
`ex1223b`	`0.0730`	`0.1300`	`0.562`	`4.57958240`	`4.57958240`	`4.57958240`	`+INF`	`0.0000`	`NA`
`fac1`	`0.3610`	`0.1000`	`3.610`	`160912612.35017100`	`160912612.35016900`	`159679950.17353800`	`+INF`	`0.0077`	`NA`
`fac3`	`53.8090`	`0.5700`	`94.402`	`31982309.84800090`	`31982309.84799990`	`31982309.84480180`	`+INF`	`0.0000`	`NA`
`fo7`	`3600.3070`	`356.4500`	`10.100`	`29.54373631`	`20.72982507`	`2.52595020`	`+INF`	`10.6961`	`NA`
`gbd`	`0.0090`	`0.0100`	`---`	`2.20000000`	`2.20000000`	`2.20000000`	`+INF`	`0.0000`	`NA`
`m3`	`1.6960`	`0.2500`	`6.784`	`37.79999992`	`37.80000000`	`37.79999908`	`+INF`	`0.0000`	`NA`
`m6`	`495.2620`	`3.5800`	`138.341`	`82.25687760`	`82.25687690`	`82.18997209`	`+INF`	`0.0008`	`NA`
`m7`	`3600.4280`	`11.6400`	`309.315`	`106.75687693`	`106.75687690`	`3.42515516`	`+INF`	`30.1685`	`NA`
`meanvarx`	`0.0820`	`0.1200`	`0.683`	`14.36923211`	`14.36923211`	`14.29621419`	`+INF`	`0.0051`	`NA`
`nvs03`	`0.0270`	`0.0100`	`---`	`16.00000000`	`16.00000000`	`16.00000000`	`+INF`	`0.0000`	`NA`
`nvs10`	`0.0170`	`0.0400`	`---`	`-310.80000000`	`-310.80000000`	`-313.20000000`	`+INF`	`0.0077`	`NA`
`o7`	`3600.4330`	`3456.2900`	`1.042`	`151.30711666`	`131.65313814`	`17.14651811`	`+INF`	`7.8244`	`NA`
`risk2bpb`	`2.2250`	`1.1000`	`2.023`	`-55.87613939`	`-55.87613940`	`-55.87613940`	`+INF`	`0.0000`	`NA`
`st_e14`	`0.1490`	`0.1100`	`1.355`	`4.57958240`	`4.57958240`	`4.57958240`	`+INF`	`0.0000`	`NA`
`st_miqp1`	`0.0310`	`0.0000`	`---`	`281.00000000`	`281.00000000`	`281.00000000`	`+INF`	`0.0000`	`NA`
`st_miqp2`	`0.0440`	`0.0400`	`---`	`2.00000000`	`2.00000000`	`2.00000000`	`+INF`	`0.0000`	`NA`
`st_miqp3`	`0.0080`	`0.0100`	`---`	`-6.00000000`	`-6.00000000`	`-6.00000000`	`+INF`	`0.0000`	`NA`
`st_miqp4`	`0.0060`	`0.0000`	`---`	`-4574.00000000`	`-4574.00000000`	`-4576.50000046`	`+INF`	`0.0005`	`NA`
`st_miqp5`	`0.0120`	`0.0400`	`---`	`-333.88888889`	`-333.88888889`	`-333.88912112`	`+INF`	`0.0000`	`NA`
`st_test5`	`0.0290`	`0.0500`	`0.580`	`-110.00000000`	`-110.00000000`	`-110.00000000`	`+INF`	`0.0000`	`NA`
`st_test6`	`0.0370`	`0.0200`	`---`	`471.00000000`	`471.00000000`	`471.00000000`	`+INF`	`0.0000`	`NA`
`st_test8`	`0.0290`	`0.0100`	`---`	`-29605.00000000`	`-29605.00000000`	`-29605.00000296`	`+INF`	`0.0000`	`NA`
`synthes1`	`0.0440`	`0.0700`	`0.629`	`6.00975902`	`6.00975891`	`6.00975891`	`+INF`	`0.0000`	`NA`
`synthes2`	`0.5900`	`0.2700`	`2.185`	`73.03531253`	`73.03531252`	`73.03531217`	`+INF`	`0.0000`	`NA`
`synthes3`	`4.3810`	`0.8500`	`5.154`	`68.00974052`	`68.00974052`	`68.00973640`	`+INF`	`0.0000`	`NA`
`tls12`	`3607.4790`	`3600.7300`	---	`mstat(14) sstat( 3)`	`mstat(14) sstat( 3)`	---	---	---	---
`tls6`	`3600.8170`	`3600.9000`	`1.000`	`20.70000003`	`38.10000000`	`1.65238095`	`+INF`	`11.5274`	`NA`
`tls7`	`3602.9820`	`3600.5500`	---	`22.50000000`	`mstat(14) sstat( 3)`	`0.25960290`	---	`85.6708`	`---`
`st_testgr1`	`1.4540`	`0.0800`	`18.175`	`-12.81160000`	`-12.81160000`	`-12.81270000`	`+INF`	`0.0001`	`NA`
`st_testgr3`	`11.1490`	`0.0600`	`185.817`	`-20.59000000`	`-20.59000000`	`-20.59000000`	`+INF`	`0.0000`	`NA`
`st_testph4`	`0.0110`	`0.0100`	`---`	`-80.50000000`	`-80.50000000`	`-80.50000000`	`+INF`	`0.0000`	`NA`
`stockcycle`	`310.0830`	`3600.2400`	`0.086`	`120532.62166667`	`123665.84333333`	`119931.67998801`	`+INF`	`0.0050`	`NA`

mean	37.69	7200.00	---	---	---	---	---	---	---
unsolved	15.79%	100.00%