private static bool[] Trace_selected_items(Problem problem, VirtualTable table)
{
var selections = new bool[problem.Items.Length];
var item_col = problem.Items.Length;
var capacity_of_row = problem.Capacity;
while (item_col > 0 && capacity_of_row > 0)
{
var i = item_col - 1;
var currObj = table[item_col, capacity_of_row];
var prevObj = table[item_col - 1, capacity_of_row];
if (currObj != prevObj)
{
selections[i] = true;
capacity_of_row -= problem.Items[i].Weight;
}
File.AppendAllText("dynamicsolver.log", string.Format("Trace: {0}\r\n", item_col));
item_col--;
}
return selections;
}
private static int Calc_objective_values(Problem problem)
{
var start = DateTime.Now;
var curr_col = new int[problem.Capacity+1];
for (var item_col = 1; item_col <= problem.Items.Length; item_col++)
{
var prev_col = curr_col;
curr_col = new int[problem.Capacity+1];
for (var capacity_of_row = 1; capacity_of_row <= problem.Capacity; capacity_of_row++)
{
var i = item_col - 1;
if (problem.Items[i].Weight <= capacity_of_row)
{
var prevObj = prev_col[capacity_of_row];
var currObj = problem.Items[i].Value +
prev_col[capacity_of_row - problem.Items[item_col - 1].Weight];
curr_col[capacity_of_row] = Math.Max(prevObj, currObj);
}
else
curr_col[capacity_of_row] = prev_col[capacity_of_row];
}
File.AppendAllText("hybridsolver.log", string.Format("Calc: {0}, {1}\r\n", item_col, DateTime.Now));
}
return curr_col[problem.Capacity];
}
public static Problem Parse(string[] text)
{
var p = new Problem();
p.Capacity = int.Parse(text[0].Split(new[]{' '}, StringSplitOptions.RemoveEmptyEntries)[1].Trim());
var n = int.Parse(text[0].Split(new[]{' '}, StringSplitOptions.RemoveEmptyEntries)[0].Trim());
p.Items = new Item[n];
var items = new List<Item>();
for (var i = 0; i < n; i++)
p.Items[i] = Item.Parse(text[i + 1]);
return p;
}
public static Solution Solve(Problem problem)
{
File.WriteAllText("dynamicsolver.log", "progress...\r\n");
using (var table = new VirtualTable(problem.Items.Length + 1, problem.Capacity + 1))
{
Calc_objective_values(problem, table);
var selection = Trace_selected_items(problem, table);
return new Solution
{
ObjectiveValue = table[problem.Items.Length, problem.Capacity],
ItemSelections = selection
};
}
}
private static int Calc_objective_values(Problem problem)
{
File.Delete("dynamicfwdsolver_diffs.log");
var total_diff_count = 0;
var start = DateTime.Now;
var curr_col = new int[problem.Capacity+1];
for (var item_col = 1; item_col <= problem.Items.Length; item_col++)
{
var prev_col = curr_col;
curr_col = new int[problem.Capacity+1];
for (var capacity_of_row = 1; capacity_of_row <= problem.Capacity; capacity_of_row++)
{
var i = item_col - 1;
if (problem.Items[i].Weight <= capacity_of_row)
{
var prevObj = prev_col[capacity_of_row];
var currObj = problem.Items[i].Value +
prev_col[capacity_of_row - problem.Items[item_col - 1].Weight];
curr_col[capacity_of_row] = Math.Max(prevObj, currObj);
}
else
curr_col[capacity_of_row] = prev_col[capacity_of_row];
}
File.AppendAllText("dynamicfwdsolver.log", string.Format("Calc {0}, {1}\r\n", item_col, DateTime.Now.Subtract(start)));
var differences = new List<int>();
for (var i = 0; i < curr_col.Length; i++)
if (prev_col[i] != curr_col[i]) differences.Add(i);
File.AppendAllText("dynamicfwdsolver_diffs.log", string.Format("item {0}: {1}\r\n", item_col - 1, differences.Count));
total_diff_count += differences.Count;
_differencesStore.Store_differences(item_col-1, differences.ToArray());
}
File.AppendAllText("dynamicfwdsolver_diffs.log", string.Format("total: {0}, avg: {1}\r\n", total_diff_count, total_diff_count / problem.Items.Length));
return curr_col[problem.Capacity];
}
public static Solution Solve(Problem problem)
{
File.WriteAllText("hybridsolver.log", "progress...\r\n");
var objectiveValue = Calc_objective_values(problem);
var selection = Decompose_objective_value(problem, objectiveValue);
var sum = 0;
for (var i = 0; i < selection.Length; i++)
if (selection[i]) sum += problem.Items[i].Value;
File.AppendAllText("hybridsolver.log", string.Format("B&B result: {0}, found: {1}\r\n", objectiveValue, sum));
return new Solution
{
ObjectiveValue = objectiveValue,
ItemSelections = selection,
IsExact = sum == objectiveValue
};
}
public static Solution Solve(Problem problem)
{
using (_differencesStore = new DifferencesStore())
{
File.WriteAllText("dynamicfwdsolver.log", "progress...\r\n");
var objectiveValue = Calc_objective_values(problem);
var selection = Trace_item_selection(problem);
var sum = 0;
for (var i = 0; i < selection.Length; i++)
if (selection[i]) sum += problem.Items[i].Value;
File.AppendAllText("dynamicfwdsolver.log",
string.Format("Trace result: {0}, found: {1}\r\n", objectiveValue, sum));
return new Solution
{
ObjectiveValue = objectiveValue,
ItemSelections = selection,
IsExact = sum == objectiveValue
};
}
}
private static void Calc_objective_values(Problem problem, VirtualTable table)
{
var start = DateTime.Now;
for (var item_col = 1; item_col <= problem.Items.Length; item_col++)
{
for (var capacity_of_row = 1; capacity_of_row <= problem.Capacity; capacity_of_row++)
{
var i = item_col - 1;
if (problem.Items[i].Weight <= capacity_of_row)
{
var prevObj = table[item_col - 1, capacity_of_row];
var currObj = problem.Items[i].Value +
table[item_col - 1, capacity_of_row - problem.Items[item_col - 1].Weight];
table[item_col, capacity_of_row] = Math.Max(prevObj, currObj);
}
else
table[item_col, capacity_of_row] = table[item_col - 1, capacity_of_row];
}
File.AppendAllText("dynamicsolver.log", string.Format("Calc: {0}, {1}\r\n", item_col, DateTime.Now));
}
}
// ergebnis ks_19_0: 4461 knoten besucht
private static bool[] Decompose_objective_value(Problem problem, int objectiveValue)
{
const double ERROR_MARGIN_PCT = 0.00002;
_errorMargin = (int)(objectiveValue * ERROR_MARGIN_PCT);
var sorted_items = problem.Items.Select((item, i) => new SortedItem{Value=item.Value, Weight=item.Weight, Index=i})
.OrderByDescending(item => item.Value).ToArray();
for (var i = sorted_items.Length - 2; i >= 0; i--)
sorted_items[i].MaxTotal = sorted_items[i + 1].Value + sorted_items[i + 1].MaxTotal;
var selected_item_indexes = new Stack<int>();
nodes_visited = 0;
Decompose_by_branching_and_bounding(objectiveValue, problem.Capacity, sorted_items, -1, selected_item_indexes);
File.AppendAllText("hybridsolver.log", string.Format("B&B: {0}, {1}\r\n", nodes_visited, DateTime.Now));
var selection = new bool[sorted_items.Length];
selected_item_indexes.ToList().ForEach(i => selection[i]=true);
return selection;
}
private static bool[] Trace_item_selection(Problem problem)
{
var selected_items = new bool[problem.Items.Length];
var remaining_capacity = problem.Capacity;
for (var i = problem.Items.Length - 1; i >= 0; i--)
{
var differences = _differencesStore.Load_differences(i);
if (differences.Length > 0)
{
if (differences.Contains(remaining_capacity))
{
selected_items[i] = true;
remaining_capacity -= problem.Items[i].Weight;
}
}
}
return selected_items;
}
