Add MTHG for Generalized Assignment Problem (#29)

* Add MTHG for Generalized Assignment Problem

* Correct README example order

* Fix README typo

README.md

@@ -13,7 +13,7 @@ Solving knapsack problems with Python using algorithms by [Martello and Toth](ht
 * Multiple 0-1 knapsack problem: MTM, MTHM
 * Change-making problem: MTC2
 * Bounded change-making problem: MTCB
-* Generalized assignment problem: MTG
+* Generalized assignment problem: MTG, MTHG
 
 Documentation is available [here](https://mknapsack.readthedocs.io).
 
@@ -141,19 +141,19 @@ res = solve_bounded_change_making(weights, n_items, capacity)
 ```python
 from mknapsack import solve_generalized_assignment
 
-# Given seven item types with the following knapsack dependent weights:
-weights = [[4, 1, 2, 1, 4, 3, 8],
-           [9, 9, 8, 1, 3, 8, 7]]
-
-# ...and the following knapsack dependent weights:
+# Given seven item types with the following knapsack dependent profits:
 profits = [[6, 9, 4, 2, 10, 3, 6],
            [4, 8, 9, 1, 7, 5, 4]]
 
-# ...and two knapsack with the following capacities:
+# ...and the following knapsack dependent weights:
+weights = [[4, 1, 2, 1, 4, 3, 8],
+           [9, 9, 8, 1, 3, 8, 7]]
+
+# ...and two knapsacks with the following capacities:
 capacities = [11, 22]
 
 # Assign items into the knapsacks while maximizing profits
-res = solve_generalized_assignment(weights, profits, capacities)
+res = solve_generalized_assignment(profits, weights, capacities)
 ```
 
 

mknapsack/_algos.f

@@ -7854,6 +7854,8 @@ subroutine mthg ( n, m, p, w, c, minmax, z, xstar, jck )
 c parameters are unchanged, but  p(i,j)  is set to  0  for all pairs
 c (i,j)  such that  w(i,j) .gt. c(i) .
 c
+cf2py intent(in) n, m, p, w, c, minmax, jck
+cf2py intent(out) z, xstar
       integer p(50,500),w(50,500),c(50),xstar(500),z
       integer zm
       integer best(500)

mknapsack/_exceptions.py

@@ -92,6 +92,13 @@ class FortranInputCheckError(Exception):
             -5: 'One or more of weights is greater than knapsack capacity',
             -6: 'One or more knapsacks cannot fit any items',
             -7: 'Number of branching trees is too small for the problem size'
+        },
+        'mthg': {
+            -1: 'Number of knapsacks is less than 2',
+            -2: 'Number of items is less than 2',
+            -3: 'Profit, weight, or capacity is <= 0',
+            -4: 'One or more of weights is greater than knapsack capacity',
+            -5: 'One or more knapsacks cannot fit any items'
         }
     }
 

mknapsack/_generalized_assignment.py

@@ -7,7 +7,7 @@
 
 import numpy as np
 
-from mknapsack._algos import mtg
+from mknapsack._algos import mtg, mthg
 from mknapsack._exceptions import FortranInputCheckError, NoSolutionError, \
     ProblemSizeError
 from mknapsack._utils import preprocess_array, pad_array
@@ -21,7 +21,7 @@ def solve_generalized_assignment(
     weights: List[List[int]],
     capacities: List[int],
     maximize: bool = True,
-    method: str = 'mtg',
+    method: str = 'mthg',
     method_kwargs: Optional[dict] = None,
     verbose: bool = False
 ) -> np.ndarray:
@@ -41,15 +41,22 @@ def solve_generalized_assignment(
         method:
             Algorithm to use for solving, should be one of
 
-                - 'mtg' - provides a fast heuristical solution that might not
-                  be the global optimum, but is suitable for larger problems,
-                  or an exact solution if required
+                - 'mtg' - provides a fast heuristical solution or an exact
+                  solution if required, but is not the most suitable for larger
+                  problems
 
                   The problem size is limited as follows:
                     * Number of knapsacks <= 10
                     * Number of items <= 100
 
-            Defaults to 'mtg'.
+                - 'mthg' - provides a fast heuristical solution that might not
+                  be the global optimum, but is suitable for larger problems
+
+                  The problem size is limited as follows:
+                    * Number of knapsacks <= 50
+                    * Number of items <= 500
+
+            Defaults to 'mthg'.
         method_kwargs:
             Keyword arguments to pass to a given `method`.
 
@@ -62,6 +69,10 @@ def solve_generalized_assignment(
                     * **check_inputs** (int, optional) - Whether to check
                       inputs or not (0=no, 1=yes). Defaults to 1.
 
+                - 'mthg'
+                    * **check_inputs** (int, optional) - Whether to check
+                      inputs or not (0=no, 1=yes). Defaults to 1.
+
             Defaults to None.
 
     Returns:
@@ -81,10 +92,10 @@ def solve_generalized_assignment(
             from mknapsack import solve_generalized_assignment
 
             res = solve_generalized_assignment(
-                weights=[[4, 1, 2, 1, 4, 3, 8],
-                         [9, 9, 8, 1, 3, 8, 7]],
                 profits=[[6, 9, 4, 2, 10, 3, 6],
                          [4, 8, 9, 1, 7, 5, 4]],
+                weights=[[4, 1, 2, 1, 4, 3, 8],
+                         [9, 9, 8, 1, 3, 8, 7]],
                 capacities=[11, 22],
                 maximize=True
             )
@@ -117,17 +128,17 @@ def solve_generalized_assignment(
         raise ValueError('Profits length must be equal to weights '
                          f'({n_profits != n_weights}')
 
+    # The problem size is limited on Fortran side
+    error_msg = (
+        'The number of {0} {1} exceeds {2}, and there is no guarantee '
+        'that the algorithm will work as intended or provides a solution '
+        'at all')
+
     method = method.lower()
     method_kwargs = method_kwargs or {}
     if method == 'mtg':
         maxm = 10
         maxn = 100
-
-        # The problem size is limited on Fortran side
-        error_msg = (
-            'The number of {0} {1} exceeds {2}, and there is no guarantee '
-            'that the algorithm will work as intended or provides a solution '
-            'at all')
         if m > maxm:
             raise ProblemSizeError(error_msg.format('knapsacks', m, maxm))
         if n > maxn:
@@ -152,9 +163,7 @@ def solve_generalized_assignment(
             back=back,
             jck=method_kwargs.get('check_inputs', 1)
         )
-        print(z)
-        print(x)
-        print(jb)
+
         if z == 0:
             raise NoSolutionError('No feasible solution found')
         elif z < 0:
@@ -166,6 +175,43 @@ def solve_generalized_assignment(
             logger.info(f'Solution vector: {x}')
             bound_name = 'Upper' if maximize else 'Lower'
             logger.info(f'{bound_name} bound: {jb}')
+    elif method == 'mthg':
+        maxm = 50
+        maxn = 500
+
+        # The problem size is limited on Fortran side
+        error_msg = (
+            'The number of {0} {1} exceeds {2}, and there is no guarantee '
+            'that the algorithm will work as intended or provides a solution '
+            'at all')
+        if m > maxm:
+            raise ProblemSizeError(error_msg.format('knapsacks', m, maxm))
+        if n > maxn:
+            raise ProblemSizeError(error_msg.format('items', n, maxn))
+
+        p = pad_array(profits, (maxm, maxn))
+        w = pad_array(weights, (maxm, maxn))
+        c = pad_array(capacities, maxm)
+
+        z, x = mthg(
+            n=n,
+            m=m,
+            p=p,
+            w=w,
+            c=c,
+            minmax=2 if maximize else 1,
+            jck=method_kwargs.get('check_inputs', 1)
+        )
+
+        if z == 0:
+            raise NoSolutionError('No feasible solution found')
+        elif z < 0:
+            raise FortranInputCheckError(method=method, z=z)
+
+        if verbose:
+            logger.info(f'Method: "{method}"')
+            logger.info(f'Total profit: {z}')
+            logger.info(f'Solution vector: {x}')
     else:
         raise ValueError(f'Given method "{method}" not known')