minor modifications

audemard · audemard · commit db7bdf9a68be · 2024-03-01T15:03:54.000+01:00
diff --git a/_private/createjson.py b/_private/createjson.py
@@ -44,7 +44,7 @@ def create_alternative_models(name, directory):
         start = False
         for line in lines:
             stripped = line.strip()
-            if stripped.startswith("minimize(") or stripped.startswith("maximize("):
+            if (stripped.startswith("minimize(") or stripped.startswith("maximize(")) and p not in ["Sudoku"]:
                 type = "COP"
             if stripped.startswith("- http"):
                 links.append(stripped[2:])
diff --git a/_private/models.json b/_private/models.json
@@ -4898,7 +4898,7 @@
       "Sum",
       "Table"
     ],
-    "type": "COP",
+    "type": "CSP",
     "tags": [
       "recreational",
       "notebook"
@@ -4956,7 +4956,7 @@
     ],
     "links": [
       "https://www.logisch-gedacht.de/logikraetsel/10stellige-zahl",
-      "https://en.wikipedia.org/wiki/Divisibility_rule)"
+      "https://en.wikipedia.org/wiki/Divisibility_rule"
     ]
   },
   {
@@ -5653,6 +5653,7 @@
     "name": "Whirlpool",
     "fullname": "academic/Whirlpool",
     "constraints": [
+      "AllDifferent",
       "Sum"
     ],
     "type": "CSP",
diff --git a/academic/AllInterval/README.md b/academic/AllInterval/README.md
@@ -26,8 +26,8 @@ the problem of finding such a series is the all-interval series problem of order
 
 ## Model
   There are two variants:
-     - a main variant;
-     - a variant 'aux' involving auxiliary variables.
+  - a main variant;
+  - a variant 'aux' involving auxiliary variables.
 
   You can also find a step-by-step modeling process in this [Jupyter notebook](http://pycsp.org/documentation/models/CSP/AllInterval/).
 
diff --git a/academic/BIBD/README.md b/academic/BIBD/README.md
@@ -8,7 +8,8 @@ It is a special case of Block Design, which also includes Latin Square problems.
 BIBD generation is described in most standard textbooks on combinatorics.
 A BIBD is defined as an arrangement of v distinct objects into b blocks such that each block contains exactly k distinct objects,
 each object occurs in exactly r different blocks, and every two distinct objects occur together in exactly l blocks.
-Another way of defining a BIBD is in terms of its incidence matrix, which is a v by b binary matrix with exactly r ones per row, k ones per column, and with a scalar product of l between any pair of distinct rows.
+Another way of defining a BIBD is in terms of its incidence matrix, which is a v by b binary matrix with exactly r ones per row, k ones per column,
+and with a scalar product of l between any pair of distinct rows.
 A BIBD is therefore specified by its parameters (v,b,r,k,l).
 
 ### Example
diff --git a/academic/Domino/README.md b/academic/Domino/README.md
@@ -7,7 +7,7 @@ Informally the Domino problem is an undirected constraint graph with a cycle and
   a pair (n,d), where n is the number of variables and d the size of the domain
 
 ## Model
-  There are two variants: a main one and a variant 'table" with constraints in extension.
+  There are two variants: a main one and a variant 'table' with constraints in extension.
 
   constraints: [AllEqual](http://pycsp.org/documentation/constraints/AllEqual), [Table](http://pycsp.org/documentation/constraints/Table)
 
diff --git a/academic/GolombRuler/README.md b/academic/GolombRuler/README.md
@@ -2,8 +2,8 @@
 
 This is [Problem 006](https://www.csplib.org/Problems/prob006/) at CSPLib.
 
-A Golomb ruler is defined as a set of n integers a1 < a2 < ... < an such that the n x (n-1)/2 differences aj - ai are distinct.
-Such a ruler is said to contain n marks (or ticks) and to be of length an.
+A Golomb ruler is defined as a set of n integers v1 < v2 < ... < vn such that the n x (n-1)/2 differences vj - vi are distinct.
+Such a ruler is said to contain n marks (or ticks) and to be of length vn.
 The objective is to find optimal rulers (i.e., rulers of minimum length).
 
 An optimal Golomb ruler with 4 ticks.
@@ -26,7 +26,7 @@ This problem (and its variants) is said to have many practical applications incl
 
   Here, there are 3 variants:
     - a main one
-    - a variant "dec" by decompoising the AllDifferent constraint
+    - a variant "dec" by decomposing the AllDifferent constraint
     - a variant "aux" with auxiliary variables
 
   constraints: [AllDifferent](http://pycsp.org/documentation/constraints/AllDifferent), [Maximum](http://pycsp.org/documentation/constraints/Maximum)
diff --git a/academic/Neighbours/README.md b/academic/Neighbours/README.md
@@ -17,7 +17,7 @@ The original MZN model was proposed by Peter J. Stuckey, with a Licence that sem
 ## Model
   There are two variants:
     - a main one with intensional constraints,
-    - a 'table" variant with extensional constraints
+    - a 'table' variant with extensional constraints
 
   Constraints: Count, Lex, Sum, Table
 
diff --git a/academic/Perfect1Factorization/README.md b/academic/Perfect1Factorization/README.md
@@ -15,7 +15,7 @@ The MZN model was proposed by Mikael Zayenz Lagerkvist (Licence at https://githu
 
 ## Execution
 ```
-  python Perfect1Factorization.py -data=[number]
+  python Perfect1Factorization.py -data=number
 ```
 
 ## Links
diff --git a/academic/QuasiGroup/README.md b/academic/QuasiGroup/README.md
@@ -3,7 +3,7 @@
 This is [Problem 003](https://www.csplib.org/Problems/prob003/) at CSPLib.
 
 An order n quasigroup is a Latin square of size n.
-That is, a n×n multiplication table in which each element occurs once in every row and column.
+That is, an n×n multiplication table in which each element occurs once in every row and column.
 A quasigroup can be specified by a set and a binary multiplication operator, ∗ defined over this set.
 Quasigroup existence problems determine the existence or non-existence of quasigroups of
 a given size with additional properties. For example:
@@ -46,80 +46,6 @@ That is, a ∗ a = a for every element a.
 
 ## Tags
   academic, notebook, csplib, xcsp22
- """
-
-from pycsp3 import *
-
-n = data or 8
-
-pairs = [(i, j) for i in range(n) for j in range(n)]
-
-# x[i][j] is the value at row i and column j of the quasi-group
-x = VarArray(size=[n, n], dom=range(n))
-
-satisfy(
-    # ensuring a Latin square
-    AllDifferent(x, matrix=True),
-
-    # ensuring idempotence  tag(idempotence)
-    [x[i][i] == i for i in range(n)]
-)
-
-if variant("base"):
-    if subvariant("v3"):
-        satisfy(
-            x[x[i][j], x[j][i]] == i for i, j in pairs
-        )
-    elif subvariant("v4"):
-        satisfy(
-            x[x[j][i], x[i][j]] == i for i, j in pairs
-        )
-    elif subvariant("v5"):
-        satisfy(
-            x[x[x[j][i], j], j] == i for i, j in pairs
-        )
-    elif subvariant("v6"):
-        satisfy(
-            x[x[i][j], j] == x[i, x[i][j]] for i, j in pairs
-        )
-    elif subvariant("v7"):
-        satisfy(
-            x[x[j][i], j] == x[i, x[j][i]] for i, j in pairs
-        )
-elif variant("aux"):
-    if subvariant("v3"):
-        y = VarArray(size=[n, n], dom=range(n * n))
-
-        satisfy(
-            [x[y[i][j]] == i for i, j in pairs if i != j],
-            [y[i][j] == x[i][j] * n + x[j][i] for i, j in pairs if i != j]
-        )
-    elif subvariant("v4"):
-        y = VarArray(size=[n, n], dom=range(n * n))
-
-        satisfy(
-            [x[y[i][j]] == i for i, j in pairs if i != j],
-            [y[i][j] == x[j][i] * n + x[i][j] for i, j in pairs if i != j]
-        )
-    elif subvariant("v5"):
-        y = VarArray(size=[n, n], dom=range(n))
-
-        satisfy(
-            [x[:, i][x[i][j]] == y[i][j] for i, j in pairs if i != j],
-            [x[:, i][y[i][j]] == j for i, j in pairs if i != j]
-        )
-    elif subvariant("v7"):
-        y = VarArray(size=[n, n], dom=range(n))
-
-        satisfy(
-            (
-                x[:, j][x[j][i]] == y[i][j],
-                x[i][x[j][i]] == y[i][j]
-            ) for i, j in pairs if i != j
-        )
-
-""" Comments
-1) note that we can post tuples of constraints instead of individually, as demonstrated in aux-v7
 
 <br />
 
diff --git a/academic/Queens/README.md b/academic/Queens/README.md
@@ -2,7 +2,7 @@
 
 This is [Problem 054](https://www.csplib.org/Problems/prob054/) at CSPLib.
 
-Can n queens (of the same colour) be placed on a n×n chessboard so that none of the queens can attack each other?
+Can n queens (of the same colour) be placed on an n×n chessboard so that none of the queens can attack each other?
 
 ## Example
   A solution for n=8
diff --git a/academic/Whirlpool/README.md b/academic/Whirlpool/README.md
@@ -3,7 +3,7 @@
 Perfect diagonal extended whirlpool permutation
 
 A whirlpool permutation is an n x m matrix containing number 1..n*m where every 2x2 sub matrix is either ordered cw (clockwise) or ccw (counter-clockwise).
-An extended whirlpool permutation requires that the outside ring is ordered cw or ccw, and the ring inside it, etc
+An extended whirlpool permutation requires that the outside ring is ordered cw or ccw, and the ring inside it, etc.
 A perfect diagonal whirlpool permutation required n = m and that the sum of both diagonals is n*(n+1)*(n+1) div 2.
 
 The model, below, is close to (can be seen as the close translation of) the one submitted to the M2020 inizinc challenge.
@@ -13,7 +13,7 @@ No Licence was explicitly mentioned (MIT Licence is assumed).
   Two integers (n,m)
 
 ## Model
-  Constraints: Sum
+  Constraints: AllDifferent, Sum
 
 ## Execution
 ```
diff --git a/crafted/LargeScaleScheduling/README.md b/crafted/LargeScaleScheduling/README.md
@@ -1,6 +1,6 @@
 # Problem LargeScaleScheduling
 
-Large scale scheduling instances have been built to show the interest of a scalable time-table filtering algorithm for the Cumulative Constraint.
+Large scale scheduling instances have been built to show the interest of a scalable timetable filtering algorithm for the Cumulative Constraint.
 
 ## Data (example)
   00100-0.json
diff --git a/crafted/MultiKnapsack/README.md b/crafted/MultiKnapsack/README.md
@@ -1,7 +1,7 @@
 # Problem MultiKnapsack
 
 The Multi dimensional knapsack problem was originally proposed as an optimization problem by the OR community.
-Here, it is the feasability version, as used, e.g., in (Refalo, CP 2004) and (Pesant et al., JAIR 2012).
+Here, it is the feasibility version, as used, e.g., in (Refalo, CP 2004) and (Pesant et al., JAIR 2012).
 
 ## Data Example
   OR05x100-25-1.json
diff --git a/realistic/Auction/README.md b/realistic/Auction/README.md
@@ -2,7 +2,10 @@
 
 Problem 063 on CSPLib.
 
-There is a bunch of people bidding for things. A bid has a value, and the bid is for a set of items. If we have two bids, call them A and B, and there is an intersection on the items they bid for, then we can accept bid A or bid B, but we cannot accept both of them. However, if A and B are bids on disjoint sets of items then these two bids are compatible with each other, and we might accept both. The problem then is to accept compatible bids such that we maximise the sum of the values of those bids (i.e. make most money).
+There is a bunch of people bidding for things. A bid has a value, and the bid is for a set of items. If we have two bids, call them A and B,
+and there is an intersection on the items they bid for, then we can accept bid A or bid B, but we cannot accept both of them.
+However, if A and B are bids on disjoint sets of items then these two bids are compatible with each other, and we might accept both.
+The problem then is to accept compatible bids such that we maximise the sum of the values of those bids (i.e. make most money).
 
 ## Data
   example.json
diff --git a/realistic/BACP/README.md b/realistic/BACP/README.md
@@ -2,7 +2,8 @@
 
 Problem 30 of the CSPLib.
 
-BACP is to design a balanced academic curriculum by assigning periods to courses in a way that the academic load of each period is balanced, i.e., as similar as possible .
+BACP is to design a balanced academic curriculum by assigning periods to courses in a way that the academic load of each period is balanced,
+i.e., as similar as possible.
 
 ## Data Example
   10.json
diff --git a/realistic/CarSequencing/README.md b/realistic/CarSequencing/README.md
@@ -1,7 +1,7 @@
 # Problem CarSequencing
 
 A number of cars are to be produced; they are not identical, because different options are available as variants on the basic model.
-The assembly line has different stations which install the various options (air-conditioning, sun-roof, etc.).
+The assembly line has different stations which install the various options (air-conditioning, sunroof, etc.).
 These stations have been designed to handle at most a certain percentage of the cars passing along the assembly line.
 Furthermore, the cars requiring a certain option must not be bunched together, otherwise the station will not be able to cope.
 Consequently, the cars must be arranged in a sequence so that the capacity of each station is never exceeded.
diff --git a/realistic/FreePizza/README.md b/realistic/FreePizza/README.md
@@ -6,7 +6,7 @@ The problem arises in the University College Cork student dorms. There is a larg
 of pizzas for a party, and many of the students have vouchers for acquiring discounts in purchasing
 pizzas. A voucher is a pair of numbers e.g. (2, 4), which means if you pay for 2 pizzas then you can
 obtain for free up to 4 pizzas as long as they each cost no more than the cheapest of the 2 pizzas you
-paid for. Similarly a voucher (3, 2) means that if you pay for 3 pizzas you can get up to 2 pizzas for
+paid for. Similarly, a voucher (3, 2) means that if you pay for 3 pizzas you can get up to 2 pizzas for
 free as long as they each cost no more than the cheapest of the 3 pizzas you paid for. The aim is to
 obtain all the ordered pizzas for the least possible cost. Note that not all vouchers need to be used.
 
diff --git a/realistic/GeneralizedMKP/README.md b/realistic/GeneralizedMKP/README.md
@@ -1,7 +1,7 @@
 # Problem GeneralizedMKP
 
 In this variation of the knapsack problem, the weight of knapsack item i is given by a D-dimensional vector
-wi = (wi1 , . . . , wiD ) and the knapsack has a D-dimensional capacity vector (W1 , . . . , WD ).
+wi = (wi1, ..., wiD ) and the knapsack has a D-dimensional capacity vector (W1, ..., WD).
 The target is to maximize the sum of the values of the items in the knapsack so that
 the sum of weights in each dimension d does not exceed Wd .
 See Wikipedia.
diff --git a/realistic/RCPSP_WET/README.md b/realistic/RCPSP_WET/README.md
@@ -2,7 +2,7 @@
 
 Resource-Constrained Project Scheduling Problems with Weighted Earliness/Tardiness objective (RCPSP/WET).
 
-The objective is to find a optimal schedule so that tasks start as close as possible to
+The objective is to find an optimal schedule so that tasks start as close as possible to
 the given start time for each task, penalizing earliness or tardiness according to
 the given weight for earliness and tardiness per task.
 
diff --git a/realistic/ShipScheduling/README.md b/realistic/ShipScheduling/README.md
@@ -1,7 +1,7 @@
 # Problem ShipScheduling
 
 From the ICAPS conference paper (whose reference is given below):
-"Ship scheduling deals with assigning arrival and departure times to a ﬂeet of ships,
+"Ship scheduling deals with assigning arrival and departure times to a fleet of ships,
 as well as the amount and sometimes type of cargo that is carried on each ship.
 One consideration in ship scheduling which does not occur in other transportation problems is that most ports have
 restrictions on the draft of ships that may safely enter the port.
diff --git a/realistic/TPP/README.md b/realistic/TPP/README.md
@@ -1,7 +1,7 @@
 # Problem TPP
 
 The goal of the asymmetric travelling purchaser problem is to decide where to buy each of a set of products,
-and in which order to visit the purchase locations, so as to minimize the total travel and purchase costs.
+and in which order to visit the purchase locations, in order to minimize the total travel and purchase costs.
 Travel costs are asymmetric, and cities are laid out on a grid with travel only allowed between horizontally and vertically adjacent cities.
 
 The model, below, is close to (can be seen as the close translation of) the one submitted to the 2012/2016 Minizinc challenges.
diff --git a/realistic/TTP_PV/README.md b/realistic/TTP_PV/README.md
@@ -1,7 +1,7 @@
 # Problem TTP_PV
 
 Traveling Tournament Problem with Predefined Venues (TTPPV).
-The problem consists of finding an optimal compact single round robin schedule for a sport event.
+The problem consists of finding an optimal compact single round-robin schedule for a sport event.
 Given a set of n teams, each team has to play against every other team exactly once.
 In each round, a team plays either at home or away, however no team can play more than three consecutive times at home or away.
 The sum of the traveling distance of each team has to be minimized.
diff --git a/recreational/Amaze/README.md b/recreational/Amaze/README.md
@@ -32,4 +32,3 @@ while paying attention to not having crossed lines.
 #### Amaze_z3.py
  - constraints: [Count](http://pycsp.org/documentation/constraints/Count)
  - tags: recreational, mzn14, mzn19
-
diff --git a/recreational/Chessboard/README.md b/recreational/Chessboard/README.md
@@ -1,6 +1,6 @@
 # Problem Chessboard
 
-Place knights, rooks, queens, bishops on a n*n chessboard so that none takes each other.
+Place knights, rooks, queens, bishops on an n*n chessboard so that none takes each other.
 
 The model, below, is close to (can be seen as the close translation of) the one submitted to the 2023 Minizinc challenge.
 No Licence was explicitly mentioned (MIT Licence assumed).
diff --git a/recreational/Diagnosis/README.md b/recreational/Diagnosis/README.md
@@ -2,7 +2,9 @@
 
 See Problem 042 on csplib.
 
-Model-based diagnosis can be seen as taking as input a partially parameterized structural description of a system and a set of observations about that system. Its output is a set of assumptions which, together with the structural description, logically imply the observations, or that are consistent with the observations.
+Model-based diagnosis can be seen as taking as input a partially parameterized structural description of a system and a set of observations about that system.
+Its output is a set of assumptions which, together with the structural description, logically imply the observations,
+or that are consistent with the observations.
 
 ## Data Example
   example.json
diff --git a/recreational/GraphMaxAcyclic/README.md b/recreational/GraphMaxAcyclic/README.md
@@ -1,6 +1,6 @@
 # Problem GraphMaxAcyclic
 
-Given a edge-weighted directed graph with possibly many cycles, the task is to find an acyclic sub-graph of maximal weight.
+Given an edge-weighted directed graph with possibly many cycles, the task is to find an acyclic sub-graph of maximal weight.
 
 ## Data Example
   example.json
diff --git a/recreational/KMedian/README.md b/recreational/KMedian/README.md
@@ -1,7 +1,7 @@
 # Problem KMedian
 
 The k-median problem (with respect to the 1-norm) is the problem of finding k centers such that the clusters formed by them are the most compact.
-Formally, given a set of data points, the k centers ci are to be chosen so as to minimize the
+Formally, given a set of data points, the k centers ci are to be chosen in order to minimize the
 sum of the distances from each data point to the nearest ci.
 
 ## Data (example)
diff --git a/recreational/Rehearsal/README.md b/recreational/Rehearsal/README.md
@@ -2,7 +2,7 @@
 
 Problem 039 on CSPLib.
 
-A concert is to consist of nine pieces of music of different durations each involving a different combination of the five members of the orchestra (from csplib).
+A concert is to consist of nine pieces of music of different durations each involving a different combination of the five members of the orchestra.
 
 ## Data
   rs.json
diff --git a/recreational/RoomMate/README.md b/recreational/RoomMate/README.md
@@ -4,7 +4,7 @@ In mathematics, economics and computer science, the stable-roommate problem is t
 A matching is a separation of the set into disjoint pairs (‘roommates’).
 The matching is stable if there are no two elements which are not roommates and which both prefer each other to their roommate under the matching.
 This is distinct from the stable-marriage problem in that the stable-roommates problem allows matches between any two elements, not just between classes of
-”men” and ”women”.
+'men' and 'women'.
 
 ## Data Example
   sr0006.json
diff --git a/recreational/RotationPuzzle/README.md b/recreational/RotationPuzzle/README.md
@@ -1,6 +1,6 @@
 # Problem RotationPuzzle
 
-This problem was called "rotation" at Nokia's web site.
+This problem was called "rotation" at Nokia's website.
 
 The puzzle is a 4x4 grid of numbers. There are four operations, each of
 which involves rotating the numbers in a 3x3 subgrid clockwise.
diff --git a/recreational/StableMarriage/README.md b/recreational/StableMarriage/README.md
@@ -4,8 +4,8 @@ Consider two groups of men and women who must marry.
 Consider that each person has indicated a ranking for her/his possible spouses.
 The problem is to find a matching between the two groups such that the marriages are stable.
 A marriage between a man m and a woman w is stable iff:
- - whenever m prefers an other woman o to w, o prefers her husband to m
- - whenever w prefers an other man o to m, o prefers his wife to w
+ - whenever m prefers another woman o to w, o prefers her husband to m
+ - whenever w prefers another man o to m, o prefers his wife to w
 
 In 1962, David Gale and Lloyd Shapley proved that, for any equal number n of men and women,
 it is always possible to make all marriages stable, with an algorithm running in O(n^2).
diff --git a/recreational/TemplateDesign/README.md b/recreational/TemplateDesign/README.md
diff --git a/recreational/TrainSchedule/README.md b/recreational/TrainSchedule/README.md
diff --git a/recreational/TravelingSalesman/README.md b/recreational/TravelingSalesman/README.md
diff --git a/recreational/TravelingTournament/README.md b/recreational/TravelingTournament/README.md
diff --git a/single/Dinner/README.md b/single/Dinner/README.md
diff --git a/single/Grocery/README.md b/single/Grocery/README.md
diff --git a/single/Purdey/README.md b/single/Purdey/README.md
diff --git a/single/SuperNumber/README.md b/single/SuperNumber/README.md
diff --git a/single/TravelingWorld/README.md b/single/TravelingWorld/README.md
