from collections import deque class BlossomAuxData: """Class to hold auxiliary data during the blossom algorithm's execution.""" def __init__(self, queue: deque, parent: list[int], base: list[int], in_blossom: list[bool], match: list[int], in_queue: list[bool]): self.queue = queue self.parent = parent self.base = base self.in_blossom = in_blossom self.match = match self.in_queue = in_queue class BlossomData: """Class to encapsulate data related to a blossom in the graph.""" def __init__(self, aux_data: BlossomAuxData, u: int, v: int, lca: int): self.aux_data = aux_data self.u = u self.v = v self.lca = lca class EdmondsBlossomAlgorithm: UNMATCHED = -1 # Constant to represent unmatched vertices @staticmethod def maximum_matching(edges: list[list[int]], vertex_count: int) -> list[list[int]]: """ Finds the maximum matching in a graph using the Edmonds Blossom Algorithm. Args: edges: A list of edges represented as pairs of vertices. vertex_count: The total number of vertices in the graph. Returns: A list of matched pairs in the form of a list of lists. """ # Create an adjacency list for the graph graph = [[] for _ in range(vertex_count)] # Populate the graph with the edges for edge in edges: u, v = edge graph[u].append(v) graph[v].append(u) # All vertices are initially unmatched match = [EdmondsBlossomAlgorithm.UNMATCHED] * vertex_count parent = [EdmondsBlossomAlgorithm.UNMATCHED] * vertex_count base = list(range(vertex_count)) # Each vertex is its own base initially in_blossom = [False] * vertex_count in_queue = [False] * vertex_count # Tracks vertices in the BFS queue # Main logic for finding maximum matching for u in range(vertex_count): # Only consider unmatched vertices if match[u] == EdmondsBlossomAlgorithm.UNMATCHED: # BFS initialization parent = [EdmondsBlossomAlgorithm.UNMATCHED] * vertex_count base = list(range(vertex_count)) in_blossom = [False] * vertex_count in_queue = [False] * vertex_count queue = deque([u]) # Start BFS from the unmatched vertex in_queue[u] = True augmenting_path_found = False # BFS to find augmenting paths while queue and not augmenting_path_found: current = queue.popleft() # Get the current vertex for y in graph[current]: # Explore adjacent vertices # Skip if we're looking at the current match if match[current] == y: continue if base[current] == base[y]: # Avoid self-loops continue if parent[y] == EdmondsBlossomAlgorithm.UNMATCHED: # Case 1: y is unmatched; we've found an augmenting path if match[y] == EdmondsBlossomAlgorithm.UNMATCHED: parent[y] = current # Update the parent augmenting_path_found = True # Augment along this path EdmondsBlossomAlgorithm.update_matching(match, parent, y) break # Case 2: y is matched; add y's match to the queue z = match[y] parent[y] = current parent[z] = y if not in_queue[z]: # If z is not already in the queue queue.append(z) in_queue[z] = True else: # Case 3: Both current and y have a parent; # check for a cycle/blossom base_u = EdmondsBlossomAlgorithm.find_base(base, parent, current, y) if base_u != EdmondsBlossomAlgorithm.UNMATCHED: EdmondsBlossomAlgorithm.contract_blossom(BlossomData( BlossomAuxData(queue, parent, base, in_blossom, match, in_queue), current, y, base_u)) # Create result list of matched pairs matching_result = [] for v in range(vertex_count): # Ensure pairs are unique if match[v] != EdmondsBlossomAlgorithm.UNMATCHED and v < match[v]: matching_result.append([v, match[v]]) return matching_result @staticmethod def update_matching(match: list[int], parent: list[int], u: int): """ Updates the matching based on the augmenting path found. Args: match: The current match list. parent: The parent list from BFS traversal. u: The vertex where the augmenting path ends. """ while u != EdmondsBlossomAlgorithm.UNMATCHED: v = parent[u] # Get the parent vertex next_match = match[v] # Store the next match match[v] = u # Update match for v match[u] = v # Update match for u u = next_match # Move to the next vertex @staticmethod def find_base(base: list[int], parent: list[int], u: int, v: int) -> int: """ Finds the base of the blossom. Args: base: The base array for each vertex. parent: The parent array from BFS. u: One endpoint of the blossom. v: The other endpoint of the blossom. Returns: The lowest common ancestor of u and v in the blossom. """ visited = [False] * len(base) # Mark ancestors of u current_u = u while True: current_u = base[current_u] visited[current_u] = True # Mark this base as visited if parent[current_u] == EdmondsBlossomAlgorithm.UNMATCHED: break current_u = parent[current_u] # Find the common ancestor of v current_v = v while True: current_v = base[current_v] if visited[current_v]: # Check if we've already visited this base return current_v current_v = parent[current_v] @staticmethod def contract_blossom(blossom_data: BlossomData): """ Contracts a blossom found during the matching process. Args: blossom_data: The data related to the blossom to be contracted. """ # Mark vertices in the blossom for x in range(blossom_data.u, blossom_data.aux_data.base[blossom_data.u] != blossom_data.lca): base_x = blossom_data.aux_data.base[x] match_base_x = blossom_data.aux_data.base[blossom_data.aux_data.match[x]] # Mark the base as in a blossom blossom_data.aux_data.in_blossom[base_x] = True blossom_data.aux_data.in_blossom[match_base_x] = True for x in range(blossom_data.v, blossom_data.aux_data.base[blossom_data.v] != blossom_data.lca): base_x = blossom_data.aux_data.base[x] match_base_x = blossom_data.aux_data.base[blossom_data.aux_data.match[x]] # Mark the base as in a blossom blossom_data.aux_data.in_blossom[base_x] = True blossom_data.aux_data.in_blossom[match_base_x] = True # Update the base for all marked vertices for i in range(len(blossom_data.aux_data.base)): if blossom_data.aux_data.in_blossom[blossom_data.aux_data.base[i]]: # Contract to the lowest common ancestor blossom_data.aux_data.base[i] = blossom_data.lca if not blossom_data.aux_data.in_queue[i]: # Add to queue if not already present blossom_data.aux_data.queue.append(i) blossom_data.aux_data.in_queue[i] = True