77 results found

1. Please consider implementing excel functions PPMT,IPMT,PMT,CUMIPMT,CUMPRINC

   These are the financial functions used by banks. The applications using math.net will be more richer if it can have below functions PPMT,IPMT,PMT,CUMIPMT,CUMPRINC

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2. Median Absolute Deviation statistics

   See https://en.wikipedia.org/wiki/Median_absolute_deviation

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3. To match and provide formulas for statistical and engineering capabilities as in Excel...

   Can provide the calculation abilities of what excel inbuilt formula does as a part of framework This include formula related to statistical and engineering capabilities.

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4. Levinson Decomposition

   Levinnson Decomposition (both symmetric and non symmetric) was available in dnAnalytics 0.2 and are highly popular in signal processing. (via alibeirami)

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5. Matrix multiply-add, like DGEMM

   BLAS has something like DGEMM which does X = aAB + b*X in one loop. This can be very fast, so allowing direct use of this would be handy.

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6. Add the "Periodic" to "SplineBoundaryCondition" on MathNet.Numerics.Interpolation

   Currently, the condition Natural/ParaboricallyTerminated/FirstDerivative/SecondDerivative are on "SplineBoundaryCondition", but I'd like to add Periodic on it(I guess this is equivalent to "csape" on MATLAB).

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7. Add full multigrid solver (FMG) for boundary value problems, e.g. the Poisson equation.

   Many simluation and some image processing tasks require solving differential equations, such as heat equation and linear elliptic equation (Poisson equation).

   In case of 2D data (images), these become partial differential equations (PDEs).

   These can be solved in linear time using multigrid preconditioner with some relaxation scheme (Gauss-Seidel, CG, biCG).

   Some reference, including source code, can be found in Press et al.: "Numerical Recipes, 3rd edition".

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8. Add an FFTSHIFT and iFFTSHIFT on a matrix similar to MATLAB

   Add an FFTSHIFT and iFFTSHIFT on a matrix similar to MATLAB

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9. Add a Fixed-Point Numeric Type

   A fixed-point numeric type is essential for deterministic calculations with fractional parts. This is essential for running accurate simulations across different machines with various CPUs, OSes, because the IEEE standard for floating-point math doesn't guarantee reproducibility across machines (http://stackoverflow.com/a/328651/154766).

   Authors of real-time strategy games in .NET have typically come up with their own fixed-point math class or found some work-around. There is no fixed-point type in the BCL even though the documentation for System.Decimal at some point labeled it so, erroneously.

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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
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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 BandedMatrix, BandedSymmetricMatrix, and SparseSymmetricMatrix classes
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    started  ·  AdminChristoph Rüegg (Admin, Math.NET) responded

    Not in mainline but in one of the forks – to be merged back into mainline once ready

13. Add support for cuda

    Is this possible to split the matrix multiplication algorithms to use processor and gpgpu all at the same time?

    It would be an amazing feature to be added.

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14. Savitzky–Golay

    ?

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15. 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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16. Implement the Efficient Global Optimisation (EGO) Algorithm for Expensive Black-box functions
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17. Collection.product

    /// <summary>
    /// Returns the cartesian product of the two collections <c>c1</c>
    /// and <c>c2</c>.
    /// </summary>
    /// <param name="c1">Should not be null.</param>
    /// <param name="c2">Should not be null.</param>
    public static ICollection Product(ICollection c1, ICollection c2)
    {
    if(c1 == null)
    {
    throw new ArgumentNullException("c1", string.Format(Resources.ArgumentNull, "c1"));
    }

            if(c2 == null)
            {
                throw new ArgumentNullException("c2", string.Format(Resources.ArgumentNull, "c2"));
            }
    
            return null;
        }
    

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18. Add primality test

    Add a fast primality test, for example based on Miller-Rabin or similar efficient method.

    3 votes

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19. Implement a peak finding algorithm
    67 votes

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20. 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.

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