13 results found

1. Implement a peak finding algorithm
   67 votes

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2. Add support for surface interpolation (2D, mesh)

   Extend the interpolation methods to support 2D surface interpolation, i.e. on a mesh. See http://community.opensourcedotnet.info/forums/t/565.aspx

   48 votes

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3. Add Rotation Matrix and Vector classes for 2D, 3D and generic sized spaces

   2D and 3D calculations could benefit from optimized functions for Rotation Matrices, 2D and 3D vectors, Solving for Transformation or Rotation Matrices, using Quaternions, and more.

   45 votes

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4. Solve a system of nonlinear equations

   aka fsolve of matlab

   33 votes

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5. Add row echelon and reduced row echelon form computation

   Computing reduced row echelon and row echelon form of a Matrix could be used with Gaussian and Gauss-Jordan elimination techniques.

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6. Investigate Mono.Simd

   The upcoming Mono.Simd assembly is nearing release. Using these functions in certain areas could give massive performance gains.

   28 votes

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7. Add stochastic processes

   Almost all modern financial analysis are built around stochastic processes. There are many processes, some general like time series and some more specific. It would be very useful for people working in the finance field.

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8. VSLab

   I think this is a great project. I am suggesting your library for VSLab (http://www.codeplex.com/vslab). I think that an F# wrapper would be great. Are you interested in contribute with your math library in creating a Matlab like environment? Have a look to the project!

   19 votes

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9. Savitzky–Golay

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   11 votes

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10. Optimized DenseLU solve

    /// <summary>
    /// Solves A*X=B for X using LU factorization.
    /// </summary>
    /// <param name="columnsOfB">The number of columns of B.</param>
    /// <param name="a">The square matrix A.</param>
    /// <param name="order">The order of the square matrix <paramref name="a"/>.</param>
    /// <param name="b">On entry the B matrix; on exit the X matrix.</param>
    /// <remarks>This is equivalent to the GETRF and GETRS LAPACK routines.</remarks>
    public static unsafe void LUSolveUnsafe(int columnsOfB, double[] aIn, int order, double[] bIn)
    {
    #region Initialize
    if (aIn == null)
    throw new ArgumentNullException("a");
    if (bIn == null)
    throw new ArgumentNullException("b");

            int i;
            int j;
            int k;
    
            double* a = stackalloc double[aIn.Length];
            double* b = stackalloc double[bIn.Length];
            int* ipiv = stackalloc int[order];
    
            // Marshal
            for (i = 0; i < aIn.Length; i++)
                a[i] = aIn[i];
    
            for (i = 0; i < bIn.Length; i++)
                b[i] = bIn[i];
            #endregion
    
            #region Factorize
            // Initialize the pivot matrix to the identity permutation.
            for (i = 0; i < order; i++)
                ipiv[i] = i;
    
            // Outer loop
            for (j = 0; j < order; j++)
            {
                int indexj = j * order;
                int indexjj = indexj + j;
    
                // Apply previous transformations.
                for (i = 0; i < order; i++)
                {
                    // Most of the time is spent in the following dot product.
                    int kmax = Math.Min(i, j);
                    double s = 0.0;
                    for (k = 0; k < kmax; k++)
                        s += a[(k * order) + i] * a[indexj + k];
    
                    a[indexj + i] -= s;
                }
    
                // Find pivot and exchange if necessary.
                int p = j;
                for (i = j + 1; i < order; i++)
                    if (Math.Abs(a[indexj + i]) > Math.Abs(a[indexj + p]))
                        p = i;
    
                if (p != j)
                {
                    for (k = 0; k < order; k++)
                    {
                        var temp = a[k * order + p];
                        a[k * order + p] = a[k * order + j];
                        a[k * order + j] = temp;
                    }
                    ipiv[j] = p;
                }
    
                // Compute multipliers.
                //if (j < order & a[indexjj] != 0.0)
                if (a[indexjj] != 0.0)
                    for (i = j + 1; i < order; i++)
                        a[indexj + i] /= a[indexjj];
            }
            #endregion
    
            #region LUSolveFactored
            // Compute the column vector  P*B
            for (i = 0; i < order; i++)
            {
                if (ipiv[i] == i)
                    continue;
    
                int p = ipiv[i];
                for (j = 0; j < columnsOfB; j++)
                {
                    var temp = b[j * order + p];
                    b[j * order + p] = b[j * order + i];
                    b[j * order + i] = temp;
                }
            }
    
            // Solve L*Y = P*B
            for (k = 0; k < order; k++)
            {
                var korder = k * order;
                for (i = k + 1; i < order; i++)
                    for (j = 0; j < columnsOfB; j++)
                    {
                        var index = j * order;
                        b[i + index] -= b[k + index] * a[i + korder];
                    }
            }
    
            // Solve U*X = Y;
            for (k = order - 1; k >= 0; k--)
            {
                var korder = k + (k * order);
                for (j = 0; j < columnsOfB; j++)
                    b[k + (j * order)] /= a[korder];
    
                korder = k * order;
                for (i = 0; i < k; i++)
                    for (j = 0; j < columnsOfB; j++)
                        b[i + j * order] -= b[k + j * order] * a[i + korder];
            }
            #endregion
    
            // Marshal
            for (i = 0; i < bIn.Length; i++)
                bIn[i] = b[i];
        }
    
        /// <summary>
        /// Solves A*X=B for X using LU factorization.
        /// </summary>
        /// <param name="columnsOfB">The number of columns of B.</param>
        /// <param name="a">The square matrix A.</param>
        /// <param name="order">The order of the square matrix <paramref name="a"/>.</param>
        /// <param name="b">On entry the B matrix; on exit the X matrix.</param>
        /// <remarks>This is equivalent to the GETRF and GETRS LAPACK routines.</remarks>
        public static void LUSolveSafe(int columnsOfB, double[] aIn, int order, double[] bIn)
        {
            #region Initialize
            if (aIn == null)
                throw new ArgumentNullException("a");
            if (bIn == null)
                throw new ArgumentNullException("b");
    
            int i;
            int j;
            int k;
    
            double[] a = new double[aIn.Length];
            int[] ipiv = new int[order];
    
            Array.Copy(aIn, a, aIn.Length);
            #endregion
    
            #region Factorize
            // Initialize the pivot matrix to the identity permutation.
            for (i = 0; i < order; i++)
                ipiv[i] = i;
    
            // Outer loop
            for (j = 0; j < order; j++)
            {
                int indexj = j * order;
                int indexjj = indexj + j;
    
                // Apply previous transformations.
                for (i = 0; i < order; i++)
                {
                    // Most of the time is spent in the following dot product.
                    int kmax = Math.Min(i, j);
                    double s = 0.0;
                    for (k = 0; k < kmax; k++)
                        s += a[(k * order) + i] * a[indexj + k];
    
                    a[indexj + i] -= s;
                }
    
                // Find pivot and exchange if necessary.
                int p = j;
                for (i = j + 1; i < order; i++)
                    if (Math.Abs(a[indexj + i]) > Math.Abs(a[indexj + p]))
                        p = i;
    
                if (p != j)
                {
                    for (k = 0; k < order; k++)
                    {
                        var temp = a[k * order + p];
                        a[k * order + p] = a[k * order + j];
                        a[k * order + j] = temp;
                    }
                    ipiv[j] = p;
                }
    
                // Compute multipliers.
                //if (j < order & a[indexjj] != 0.0)
                if (a[indexjj] != 0.0)
                    for (i = j + 1; i < order; i++)
                        a[indexj + i] /= a[indexjj];
            }
            #endregion
    
            #region LUSolveFactored
            // Compute the column vector  P*B
            for (i = 0; i < order; i++)
            {
                if (ipiv[i] == i)
                    continue;
    
                int p = ipiv[i];
                for (j = 0; j < columnsOfB; j++)
                {
                    var temp = bIn[j * order + p];
                    bIn[j * order + p] = bIn[j * order + i];
                    bIn[j * order + i] = temp;
                }
            }
    
            // Solve L*Y = P*B
            for (k = 0; k < order; k++)
            {
                var korder = k * order;
                for (i = k + 1; i < order; i++)
                    for (j = 0; j < columnsOfB; j++)
                    {
                        var index = j * order;
                        bIn[i + index] -= bIn[k + index] * a[i + korder];
                    }
            }
    
            // Solve U*X = Y;
            for (k = order - 1; k >= 0; k--)
            {
                var korder = k + (k * order);
                for (j = 0; j < columnsOfB; j++)
                    bIn[k + (j * order)] /= a[korder];
    
                korder = k * order;
                for (i = 0; i < k; i++)
                    for (j = 0; j < columnsOfB; j++)
                        bIn[i + j * order] -= bIn[k + j * order] * a[i + korder];
            }
            #endregion
        }
    

    /// <summary>
    /// Solves A*X=B for X using LU factorization.
    /// </summary>
    /// <param name="columnsOfB">The number of columns of B.</param>
    /// <param name="a">The square matrix A.</param>
    /// <param name="order">The order of the square matrix <paramref name="a"/>.</param>
    /// <param name="b">On entry the B matrix; on exit the X matrix.</param>
    /// <remarks>This is equivalent to the GETRF and GETRS LAPACK routines.</remarks>
    public static unsafe void LUSolveUnsafe(int columnsOfB, double[] aIn, int order, double[] bIn)
    {
    #region Initialize
    if (aIn == null)
    throw new ArgumentNullException("a");
    if (bIn == null)
    throw new ArgumentNullException("b");

            int i;
            int j;
            int k;
    
            double* a = stackalloc double[aIn.Length];
            double*

    … more
    6 votes

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11. boost performance by adding parallel implementations
    3 votes

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    under review  ·  AdminChristoph Rüegg (Admin, Math.NET) responded

    We do some computations in parallel, however, in practice parallel execution is often not very efficient (other than e.g. concurrent execution at application level). However, we should review places where parallelization would be interesting for v3

12. Add interchange/swap function to Matrix class

    Add interchange/swap function to Matrix class which would allow swapping two rows or columns in the matrix (i.e. m.swapRows(2,3)).

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13. Histogram constructor with buckets. Data is applied to the buckets.

    I need to create a histogram with buckets from another histogram and expect my data to be counted relative to those buckets.

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