#### Decisions Convolutions

m
```using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;

namespace Algorithms.Other
{
/// <summary>
/// Almost all real complex decision-making task is described by more than one criterion.
/// There are different methods to select the best decisions from the defined set of decisions.
/// This class contains implementations of the popular convolution methods: linear and maxmin.
/// </summary>
public static class DecisionsConvolutions
{
/// <summary>
/// This method implements the linear method of decision selection. It is based on
/// the calculation of the "value" for each decision and the selection of the most
/// valuable one.
/// </summary>
/// <param name="matrix">Contains a collection of the criteria sets.</param>
/// <param name="priorities">Contains a set of priorities for each criterion.</param>
/// <returns>The most effective decision that is represented by a set of criterias.</returns>
public static List<decimal> Linear(List<List<decimal>> matrix, List<decimal> priorities)
{
var decisionValues = new List<decimal>();

foreach (var decision in matrix)
{
decimal sum = 0;
for (int i = 0; i < decision.Count; i++)
{
sum += decision[i] * priorities[i];
}

}

decimal bestDecisionValue = decisionValues.Max();
int bestDecisionIndex = decisionValues.IndexOf(bestDecisionValue);

return matrix[bestDecisionIndex];
}

/// <summary>
/// This method implements maxmin method of the decision selection. It is based on
/// the calculation of the least criteria value and comparison of decisions based
/// on the calculated results.
/// </summary>
/// <param name="matrix">Contains a collection of the criteria sets.</param>
/// <param name="priorities">Contains a set of priorities for each criterion.</param>
/// <returns>The most effective decision that is represented by a set of criterias.</returns>
public static List<decimal> MaxMin(List<List<decimal>> matrix, List<decimal> priorities)
{
var decisionValues = new List<decimal>();

foreach (var decision in matrix)
{
decimal minValue = decimal.MaxValue;
for (int i = 0; i < decision.Count; i++)
{
decimal result = decision[i] * priorities[i];
if (result < minValue)
{
minValue = result;
}
}

}

decimal bestDecisionValue = decisionValues.Max();
int bestDecisionIndex = decisionValues.IndexOf(bestDecisionValue);

return matrix[bestDecisionIndex];
}
}
}
```  