Total time (CPU seconds): 0.02 (Wallclock seconds): 0.The Knapsack problem is probably one of the most interesting and most popular in computer science, especially when we talk about dynamic programming. TwoMirCuts was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds) ![]() MixedIntegerRounding2 was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)įlowCover was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds) Knapsack was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)Ĭlique was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds) Gomory was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds) Probing was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds) 1965 iterations 1Ĭbc0038I Branch and bound needed to clear up 1 general integersĬbc0038I Full problem 1 rows 542 columns, reduced to 1 rows 128 columnsĬbc0038I Before mini branch and bound, 540 integers at bound fixed and 0 continuousĬbc0038I Mini branch and bound did not improve solution (0.02 seconds)Ĭbc0038I After 0.02 seconds - Feasibility pump exiting with objective of -1965 - took 0.01 secondsĬbc0012I Integer solution of -1965 found by feasibility pump after 0 iterations and 0 nodes (0.02 seconds)Ĭbc0038I Full problem 1 rows 542 columns, reduced to 1 rows 2 columnsĬbc0001I Search completed - best objective -1965, took 0 iterations and 0 nodes (0.02 seconds)Ĭbc0035I Maximum depth 0, 362 variables fixed on reduced costĬuts at root node changed objective from -1965.33 to -1965.33 X_sol = np.array(()).astype(int) # assumes there is oneĬommand line - ICbcModel -solve -quit (default strategy 1)Ĭontinuous objective value is -1965.33 - 0.00 secondsĬgl0004I processed model has 1 rows, 542 columns (542 integer (366 of which binary)) and 542 elementsĬutoff increment increased from 1e-05 to 0.9999Ĭbc0038I Initial state - 1 integers unsatisfied sum - 0.333333Ĭbc0038I Pass 1: suminf. Model += sp.eye(n) * x Cbc model / LP -> MIP ![]() Model += sp.eye(n) * x >= np.zeros(n) # could be improved X = model.addVariable('x', n, isInt=True) Value = np.random.randint(10, size = 1000) Weight = np.random.randint(10, size = 1000) hard instances (huge variance!) and because of that, data to check against is important! One can observe, that there is no real integrality-gap for this example. ![]() The example here just solves one problem as defined by OP using a PRNG-seed of 1, where it takes 0.02 seconds, but that's not a scientific test! NP-hard problems are all about easy vs. bounded / unbounded knapsack-versions are easily handled by just modifying the bounds.as the logs show: example is too simple to effect in their usage!. ![]()
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