CSE505/Assignment2

Assignment 1

Part A

clingo

clingo tsp.lp

size	ans	time
5	38	0.001
10	48	0.050
15	29	0.351
20	unknown	too long to solve

time grows exponentialy.

swi-prolog

size	ans	time
5	38	very fast
10	48	10+s
15	unknow	too long to solve
20	unknown	too long to solve

I didn't find a way to calculate runtime in swi-prolog. But it is clearly to see that it is way slower than clingo.

Google OR-Tools

1. Local Optimum

size	ans	time
5	38	0.0077
10	48	0.0085
15	37	0.009
20	48	0.010

2. Global Optimum

size	ans	time
5	38	time limit exceeded
10	48	time limit exceeded
15	29	time limit exceeded
20	46	time limit exceeded

AMPL

size	ans	time
5	38	very fast
10	48	very fast
15	29	very fast
20	46	very fast

Part B

clingo

clingo task.lp

size	processors	deadline	result	time
10	3	20	yes	0.031
20	6	30	no	4.498
20	8	30	yes	0.233
30	6	60	unknown	too long to solve
30	8	60	yes	2.961
30	8	80	yes	6.701
30	8	90	yes	9.245

With either too high or too low constrain, the runtime grows exponentially. If the constrain is too low, it must iterate through all answers to find one correct result. If it is too high, then it may waste too much time fill the first processor.

Google OR-Tools

size	processors	deadline	result	time
10	3	20	yes	0.035
20	6	30	no	0.037
20	8	30	yes	0.0474
30	6	60	no	0.0413
30	8	60	yes	0.0469
30	8	80	yes	0.0481
30	8	90	yes	0.0552

Unlike clingo, Google OR-Tools is much faster on this question. And too high/low constrain does not increase the run time by a lot.

Part C

clingo

clingo cut.lp

size	ans	time
5	5	0.678
10	9	0.866
15	13	1.122
20	17	1.902
25	21	6.379
30	25	34.466

One important factor of the runtime is the max number of cut to check. If we set max = 10 with size = 30, we get the time of 0.914. However, it is impossible to get the max size before running, I believe this time would be a great representation of the runtime of clingo.

AMPL

size	ans	time
5	5	very fast
10	9	very fast
15	13	very fast
20	17	very fast
25	21	very fast
30	25	very fast
100000	80393	very fast