A solver for some knapsack problems:
- 0-1 knapsack problem
- Subset sum problem
- Multiple-choice subset sum problem
These problems often appear as subproblems of more complex problems. For example:
- To generate cuts in branch-and-cut algorithms
- To solve pricing problems in column generation algorithms
- To lift bin and item dimensions in packing problems
And therefore, having efficient algorithms with reliable implementations to solve them is very useful.
The goal of this repository is to provide such efficient and reliable implementations.
Here are some usage examples of this library:
- Lifting the length of a bin in a 3D packing problem
- Solving a 0-1 knapsack subproblem inside an algorithm for a geometrical variable-sized bin packing problem
- Solving the pricing problem inside a column generation algorithm for the cutting stock problem, the multiple knapsack problem, or the genralized assignment problem
- Solving a 0-1 knapsack subproblem inside an algorithm for the packing while travelling problem
-
Greedy
O(n)-a greedy
-
Upper bounds
- Dantzig upper bound
-a dantzig
- Dantzig upper bound
-
Dynamic Programming
- Bellman
- Recursive
-a dynamic-programming-bellman-rec - Array (only optimal value)
-a dynamic-programming-bellman-array - Array + parallel (only optimal value)
-a dynamic-programming-bellman-array-parallel - Array (all)
-a dynamic-programming-bellman-array-all - Array (one)
-a dynamic-programming-bellman-array-one - Array (partial solution)
-a dynamic-programming-bellman-array-part - Array (recursive scheme)
-a dynamic-programming-bellman-array-rec - List (only optimal value)
-a dynamic-programming-bellman-list --sort 0
- Recursive
- Primal-dual (minknap)
- List (partial solution)
-a "dynamic-programming-primal-dual --partial-solution-size 64 --pairing 0"
- List (partial solution)
- Bellman
- Dynamic programming
- Bellman
- Array
-a dynamic-programming-bellman-array - List (only optimal value)
-a dynamic-programming-bellman-list - Word RAM (only optimal value)
-a dynamic-programming-bellman-word-ram - Word RAM with recursive scheme
-a dynamic-programming-bellman-word-ram-rec
- Array
- Balancing
- Array (only optimal value)
-a dynamic-programming-balancing-array
- Array (only optimal value)
- Bellman
- Dynamic programming
- Bellman
- Array
-a dynamic-programming-bellman-array - Word RAM (only optimal value)
-a dynamic-programming-bellman-word-ram - Word RAM with recursive scheme
-a dynamic-programming-bellman-word-ram-rec
- Array
- Bellman
Compile:
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release --parallel
cmake --install build --config Release --prefix installDownload data:
python3 scripts/download_data.pySolve:
./install/bin/knapsacksolver_knapsack --verbosity-level 1 --algorithm dynamic-programming-primal-dual --input data/knapsack/largecoeff/knapPI_2_10000_10000000/knapPI_2_10000_10000000_50.csv --format pisinger====================================
KnapsackSolver
====================================
Problem
-------
Knapsack problem
Instance
--------
Number of items: 10000
Capacity: 24537085856
Highest item profit: 10972919
Highest item weight: 9999761
Total item profit: 49624323576
Total item weight: 49564913430
Weight ratio: 2.02
Algorithm
---------
Dynamic programming - primal-dual - partial
Parameters
----------
Time limit: inf
Messages
Verbosity level: 1
Standard output: 1
File path:
# streams: 0
Logger
Has logger: 0
Standard error: 0
File path:
Greedy: 1
Pairing: 0
Partial solution size: 64
Time (s) Sol. Value Bound Gap Gap (%) Comment
-------- ---- ----- ----- --- ------- -------
0.000 1 0 49624323576 4.96243e+10 100.00
0.000 1 27018043776 49624323576 2.26063e+10 45.55 greedy
0.000 1 27018043776 27018127904 84128 0.00 dantzig upper bound
0.000 1 27018043776 27018127851 84075 0.00 it 1 (bound)
0.000 1 27018043776 27018127670 83894 0.00 it 3 (bound)
0.000 1 27018043776 27018127668 83892 0.00 it 5 (bound)
0.001 1 27018043776 27018127663 83887 0.00 it 7 (bound)
0.001 0 27018069931 27018127663 57732 0.00 it 8 (value)
0.001 0 27018069931 27018127651 57720 0.00 it 9 (bound)
0.001 0 27018115040 27018127651 12611 0.00 it 10 (value)
0.001 0 27018115040 27018127648 12608 0.00 it 11 (bound)
0.001 0 27018115040 27018127635 12595 0.00 it 13 (bound)
0.002 0 27018115040 27018127634 12594 0.00 it 15 (bound)
0.002 0 27018115176 27018127634 12458 0.00 it 16 (value)
0.002 0 27018117035 27018127634 10599 0.00 it 17 (value)
0.002 0 27018117035 27018127633 10598 0.00 it 17 (bound)
0.003 0 27018119811 27018127633 7822 0.00 it 18 (value)
0.003 0 27018119811 27018127631 7820 0.00 it 19 (bound)
0.003 0 27018119811 27018127629 7818 0.00 it 21 (bound)
0.003 0 27018121352 27018127629 6277 0.00 it 22 (value)
0.004 0 27018121352 27018127623 6271 0.00 it 25 (bound)
0.004 0 27018121352 27018127620 6268 0.00 it 27 (bound)
0.004 0 27018121352 27018127615 6263 0.00 it 29 (bound)
0.004 0 27018121352 27018127611 6259 0.00 it 33 (bound)
0.004 0 27018121498 27018127611 6113 0.00 it 34 (value)
0.005 0 27018121498 27018127595 6097 0.00 it 35 (bound)
0.005 0 27018121498 27018127594 6096 0.00 it 37 (bound)
0.005 0 27018121498 27018127588 6090 0.00 it 39 (bound)
0.005 0 27018121498 27018127576 6078 0.00 it 43 (bound)
0.005 0 27018121498 27018127572 6074 0.00 it 45 (bound)
0.006 0 27018121498 27018127570 6072 0.00 it 47 (bound)
0.006 0 27018121498 27018127565 6067 0.00 it 49 (bound)
0.006 0 27018121498 27018127563 6065 0.00 it 51 (bound)
0.006 0 27018121498 27018127556 6058 0.00 it 53 (bound)
0.006 0 27018121498 27018127553 6055 0.00 it 55 (bound)
0.006 0 27018121498 27018127552 6054 0.00 it 57 (bound)
0.006 0 27018121928 27018127552 5624 0.00 it 58 (value)
0.006 0 27018121928 27018127548 5620 0.00 it 61 (bound)
0.006 0 27018121928 27018127539 5611 0.00 it 63 (bound)
0.006 0 27018121928 27018127536 5608 0.00 it 64 (bound)
0.006 0 27018121928 27018127533 5605 0.00 it 65 (bound)
0.006 0 27018121928 27018127527 5599 0.00 it 66 (bound)
0.006 0 27018121928 27018127512 5584 0.00 it 67 (bound)
0.006 0 27018121928 27018127511 5583 0.00 it 68 (bound)
0.006 0 27018121928 27018127499 5571 0.00 it 69 (bound)
0.006 0 27018121928 27018127489 5561 0.00 it 70 (bound)
0.006 0 27018121928 27018127442 5514 0.00 it 71 (bound)
0.006 0 27018121928 27018127379 5451 0.00 it 72 (bound)
0.006 0 27018121928 27018127109 5181 0.00 it 73 (bound)
0.006 0 27018121928 27018127103 5175 0.00 it 74 (bound)
0.007 0 27018122233 27018127103 4870 0.00 it 75 (value)
0.007 0 27018122468 27018127103 4635 0.00 it 75 (value)
0.007 0 27018122468 27018126889 4421 0.00 it 75 (bound)
0.007 0 27018122468 27018126854 4386 0.00 it 76 (bound)
0.007 0 27018122468 27018126307 3839 0.00 it 77 (bound)
0.007 0 27018122468 27018125684 3216 0.00 it 78 (bound)
0.007 0 27018122468 27018122468 0 0.00 it 79 (bound)
0.007 1 27018122468 27018122468 0 0.00 algorithm end (solution)
Final statistics
----------------
Value: 27018122468
Has solution: 1
Bound: 27018122468
Absolute optimality gap: 0
Relative optimality gap (%): 0
Time (s): 0.00667005
Number of recursive calls: 2
Solution
--------
Number of items: 4959 / 10000 (49.59%)
Weight: 24537085626 / 24537085856 (100%)
Profit: 27018122468
Feasible: 1
./install/bin/knapsacksolver_subset_sum --verbosity-level 1 --input data/subset_sum/pthree/pthree_1000_1 --algorithm dynamic-programming-bellman-word-ram-rec====================================
KnapsackSolver
====================================
Problem
-------
Subset sum problem
Instance
--------
Number of items: 1000
Capacity: 250000
Algorithm
---------
Dynamic programming - Bellman - word RAM - recursive scheme
Parameters
----------
Time limit: inf
Messages
Verbosity level: 1
Standard output: 1
File path:
# streams: 0
Logger
Has logger: 0
Standard error: 0
File path:
Time (s) Sol. Value Bound Gap Gap (%) Comment
-------- ---- ----- ----- --- ------- -------
0.000 1 0 250000 250000 100.00
0.033 1 250000 250000 0 0.00 algorithm end (solution)
Final statistics
----------------
Value: 250000
Has solution: 1
Bound: 250000
Absolute optimality gap: 0
Relative optimality gap (%): 0
Time (s): 0.0330949
Solution
--------
Number of items: 484 / 1000 (48.4%)
Weight: 250000 / 250000 (100%)
Feasible: 1
Run tests:
export KNAPSACK_DATA=$(pwd)/data/knapsack
cd build/test
ctest --parallel
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent
