Merge pull request #999 from boostorg/develop

Merge develop for 1.83
boostorg · Jun 28, 2023 · 1a7be5d · 1a7be5d
2 parents c56f334 + 8bb0d16
commit 1a7be5d
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diff --git a/.drone.star b/.drone.star
@@ -16,13 +16,15 @@ windowsglobalimage="cppalliance/dronevs2019"
 def main(ctx):
 
   things_to_test = [ "special_fun", "distribution_tests", "mp", "misc", "interpolators", "quadrature", "autodiff", "long-running-tests", "float128_tests" ]
+  gcc13_things_to_test = [ "special_fun", "distribution_tests", "mp", "misc", "interpolators", "quadrature", "autodiff", "long-running-tests", "float128_tests", "new_floats" ]
   sanitizer_test = [ "special_fun", "distribution_tests", "misc", "interpolators", "quadrature", "float128_tests" ]
   gnu_5_stds = [ "gnu++14", "c++14" ]
   gnu_6_stds = [ "gnu++14", "c++14", "gnu++17", "c++17" ]
   clang_6_stds = [ "c++14", "c++17" ]
   gnu_9_stds = [ "gnu++14", "c++14", "gnu++17", "c++17", "gnu++2a", "c++2a" ]
   clang_10_stds = [ "c++14", "c++17", "c++2a" ]
   gnu_non_native = [ "gnu++17" ]
+  gcc13_stds = [ "c++23" ]
 
   result = []
 
@@ -59,6 +61,9 @@ def main(ctx):
       result.append(linux_cxx("Ubuntu g++ ARM64" + cxx + " " + suite, "g++", packages="g++", buildtype="boost", image="cppalliance/droneubuntu2204:multiarch", arch="arm64", environment={'TOOLSET': 'gcc', 'COMPILER': 'g++', 'CXXSTD': cxx, 'TEST_SUITE': suite, }, globalenv=globalenv))
     for cxx in gnu_non_native:
       result.append(osx_cxx("M1 Clang " + cxx + " " + suite, "clang++", buildscript="drone", buildtype="boost", xcode_version="14.1", environment={'TOOLSET': 'clang', 'CXXSTD': cxx, 'TEST_SUITE': suite, 'DEFINE': 'BOOST_MATH_NO_REAL_CONCEPT_TESTS,BOOST_MATH_NO_LONG_DOUBLE_MATH_FUNCTIONS,BOOST_MATH_MULTI_ARCH_CI_RUN', }, globalenv=globalenv))
+  for suite in gcc13_things_to_test:
+    for cxx in gcc13_stds:
+      result.append(linux_cxx("Ubuntu g++-13 " + cxx + " " + suite, "g++-13", packages="g++-13", buildtype="boost", image="cppalliance/droneubuntu2304:1", environment={'TOOLSET': 'gcc', 'COMPILER': 'g++-13', 'CXXSTD': cxx, 'TEST_SUITE': suite, }, globalenv=globalenv))
 
   return result
 

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -9,6 +9,7 @@ on:
     branches:
       - master
       - develop
+      - feature/**
   pull_request:
   release:
     types: [published, created, edited]
@@ -47,7 +48,7 @@ jobs:
         if: steps.retry1.outcome=='failure'
         run: sudo apt-add-repository -y "ppa:ubuntu-toolchain-r/test"
       - name: Install packages
-        run: sudo apt install g++-12 clang-14 libgmp-dev libmpfr-dev libfftw3-dev
+        run: sudo apt-get install -y g++-12 clang-14 libgmp-dev libmpfr-dev libfftw3-dev
       - name: Checkout main boost
         run: git clone -b develop --depth 1 https://github.com/boostorg/boost.git ../boost-root
       - name: Update tools/boostdep
@@ -75,7 +76,7 @@ jobs:
         run: ./config_info_travis
         working-directory: ../boost-root/libs/config/test
       - name: Test
-        run: ../../../b2 toolset=$TOOLSET ${{ matrix.suite }} define=CI_SUPPRESS_KNOWN_ISSUES define=SLOW_COMPILER
+        run: ../../../b2 toolset=$TOOLSET ${{ matrix.suite }} define=CI_SUPPRESS_KNOWN_ISSUES define=SLOW_COMPILER define=BOOST_MATH_RUN_MP_TESTS
         working-directory: ../boost-root/libs/math/test
   ubuntu-focal-no-eh:
     runs-on: ubuntu-20.04
@@ -105,7 +106,7 @@ jobs:
         if: steps.retry1.outcome=='failure'
         run: sudo apt-add-repository -y "ppa:ubuntu-toolchain-r/test"
       - name: Install packages
-        run: sudo apt install g++-9 g++-11 clang-9 clang-10 libgmp-dev libmpfr-dev libfftw3-dev
+        run: sudo apt-get install -y g++-9 g++-11 clang-9 clang-10 libgmp-dev libmpfr-dev libfftw3-dev
       - name: Checkout main boost
         run: git clone -b develop --depth 1 https://github.com/boostorg/boost.git ../boost-root
       - name: Update tools/boostdep
@@ -214,7 +215,7 @@ jobs:
         run: config_info_travis
         working-directory: ../boost-root/libs/config/test
       - name: Test
-        run: ..\..\..\b2 --hash %ARGS% define=CI_SUPPRESS_KNOWN_ISSUES debug-symbols=off ${{ matrix.suite }}
+        run: ..\..\..\b2 --hash %ARGS% define=CI_SUPPRESS_KNOWN_ISSUES debug-symbols=off ${{ matrix.suite }} pch=off
         working-directory: ../boost-root/libs/math/test
   windows_gcc:
     runs-on: windows-2019
@@ -299,7 +300,7 @@ jobs:
         run: config_info_travis
         working-directory: ../boost-root/libs/config/test
       - name: Test
-        run: ..\..\..\b2 --hash %ARGS% define=CI_SUPPRESS_KNOWN_ISSUES ${{ matrix.suite }}
+        run: ..\..\..\b2 --hash %ARGS% define=CI_SUPPRESS_KNOWN_ISSUES ${{ matrix.suite }} pch=off
         working-directory: ../boost-root/libs/math/test
   cygwin:
     runs-on: windows-latest
@@ -364,7 +365,7 @@ jobs:
         if: steps.retry1.outcome=='failure'
         run: sudo apt-add-repository -y "ppa:ubuntu-toolchain-r/test"
       - name: Install packages
-        run: sudo apt install g++-10 libgmp-dev libmpfr-dev libfftw3-dev
+        run: sudo apt-get install -y g++-10 libgmp-dev libmpfr-dev libfftw3-dev
       - name: Checkout main boost
         run: git clone -b develop --depth 1 https://github.com/boostorg/boost.git ../boost-root
       - name: Update tools/boostdep
@@ -404,7 +405,7 @@ jobs:
         if: steps.retry1.outcome=='failure'
         run: sudo apt-add-repository -y "ppa:ubuntu-toolchain-r/test"
       - name: Install packages
-        run: sudo apt install clang-10 libgmp-dev libmpfr-dev libfftw3-dev
+        run: sudo apt-get install -y clang-10 libgmp-dev libmpfr-dev libfftw3-dev libtbb-dev
       - name: Checkout main boost
         run: git clone -b develop --depth 1 https://github.com/boostorg/boost.git ../boost-root
       - name: Update tools/boostdep
@@ -448,7 +449,7 @@ jobs:
         if: steps.retry1.outcome=='failure'
         run: sudo apt-add-repository -y "ppa:ubuntu-toolchain-r/test"
       - name: Install packages
-        run: sudo apt install g++-10 libgmp-dev libmpfr-dev libfftw3-dev
+        run: sudo apt-get install -y g++-10 libgmp-dev libmpfr-dev libfftw3-dev
       - name: Checkout main boost
         run: git clone -b develop --depth 1 https://github.com/boostorg/boost.git ../boost-root
       - name: Update tools/boostdep

diff --git a/README.md b/README.md
@@ -2,8 +2,7 @@ Boost Math Library
 [![Build Status](https://drone.cpp.al/api/badges/boostorg/math/status.svg)](https://drone.cpp.al/boostorg/math)[![Build Status](https://github.com/boostorg/math/workflows/CI/badge.svg?branch=develop)](https://github.com/boostorg/math/actions)
 ==================
 
->ANNOUNCEMENT: Support for C++11 will be deprecated in this library starting in July 2023 (Boost 1.82).  
->New features will require *at least* C++14, as will existing features starting with the deprecation release.
+>ANNOUNCEMENT: This library now requires a compliant C++14 compiler.
 
 This library is divided into several interconnected parts:
 

diff --git a/doc/background/references.qbk b/doc/background/references.qbk
@@ -34,7 +34,7 @@ The Wolfram Research Documentation Center is a collection of online reference ma
 Statistical Distributions (Wiley Series in Probability & Statistics) (Paperback)
 by N.A.J. Hastings, Brian Peacock, Merran Evans, ISBN: 0471371246, Wiley 2000.
 
-[@http://www.worldscibooks.com/mathematics/p191.html Extreme Value Distributions, Theory and Applications]
+[@https://www.google.com/books/edition/Extreme_Value_Distributions/GwBqDQAAQBAJ?hl=en&gbpv=0 Extreme Value Distributions, Theory and Applications]
 Samuel Kotz & Saralees Nadarajah, ISBN 978-1-86094-224-2 & 1-86094-224-5 Oct 2000,
 Chapter 1.2 discusses the various extreme value distributions.
 

diff --git a/doc/distributions/exponential.qbk b/doc/distributions/exponential.qbk
@@ -93,7 +93,7 @@ In the following table [lambda] is the parameter lambda of the distribution,
 (See also the reference documentation for the related __extreme_distrib.)
 
 * 
-[@http://www.worldscibooks.com/mathematics/p191.html Extreme Value Distributions, Theory and Applications
+[@https://www.google.com/books/edition/Extreme_Value_Distributions/GwBqDQAAQBAJ?hl=en&gbpv=0 Extreme Value Distributions, Theory and Applications
 Samuel Kotz & Saralees Nadarajah]
 discuss the relationship of the types of extreme value distributions.
 

diff --git a/doc/distributions/extreme_value.qbk b/doc/distributions/extreme_value.qbk
@@ -38,7 +38,7 @@ websites.
 
 The relationship of the types of extreme value distributions, of which this is but one, is
 discussed by
-[@http://www.worldscibooks.com/mathematics/p191.html Extreme Value Distributions, Theory and Applications
+[@https://www.google.com/books/edition/Extreme_Value_Distributions/GwBqDQAAQBAJ?hl=en&gbpv=0 Extreme Value Distributions, Theory and Applications
 Samuel Kotz & Saralees Nadarajah].
 
 The distribution has a PDF given by:

diff --git a/doc/distributions/hypergeometric.qbk b/doc/distributions/hypergeometric.qbk
@@ -15,11 +15,11 @@
       typedef RealType value_type;
       typedef Policy   policy_type;
       // Construct:
-      hypergeometric_distribution(unsigned r, unsigned n, unsigned N); // r=defective/failures/success, n=trials/draws, N=total population.
+      hypergeometric_distribution(uint64_t r, uint64_t n, uint64_t N); // r=defective/failures/success, n=trials/draws, N=total population.
       // Accessors:
-      unsigned total()const;
-      unsigned defective()const;
-      unsigned sample_count()const;
+      uint64_t total()const;
+      uint64_t defective()const;
+      uint64_t sample_count()const;
    };
 
    typedef hypergeometric_distribution<> hypergeometric;
@@ -56,20 +56,20 @@ then we obtain basically the same graphs:
 
 [h4 Member Functions]
 
-   hypergeometric_distribution(unsigned r, unsigned n, unsigned N);
+   hypergeometric_distribution(uint64_t r, uint64_t n, uint64_t N);
 
 Constructs a hypergeometric distribution with a population of /N/ objects,
 of which /r/ are defective, and from which /n/ are sampled.
 
-   unsigned total()const;
+   uint64_t total()const;
 
 Returns the total number of objects /N/.
 
-   unsigned defective()const;
+   uint64_t defective()const;
 
 Returns the number of objects /r/ in population /N/ which are defective.
 
-   unsigned sample_count()const;
+   uint64_t sample_count()const;
 
 Returns the number of objects /n/ which are sampled from the population /N/.
 
@@ -87,7 +87,7 @@ and Python
 All the [link math_toolkit.dist_ref.nmp usual non-member accessor functions]
 that are generic to all distributions are supported: __usual_accessors.
 
-The domain of the random variable is the unsigned integers in the range
+The domain of the random variable are the 64-bit unsigned integers in the range
 \[max(0, n + r - N), min(n, r)\].  A __domain_error is raised if the
 random variable is outside this range, or is not an integral value.
 

diff --git a/doc/distributions/triangular.qbk b/doc/distributions/triangular.qbk
@@ -38,7 +38,7 @@ The triangular distribution is often used where the distribution is only vaguely
 but, like the [@http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29 uniform distribution],
 upper and limits are 'known', but a 'best guess', the mode or center point, is also added.
 It has been recommended as a
-[@http://www.worldscibooks.com/mathematics/etextbook/5720/5720_chap1.pdf proxy for the beta distribution.]
+[@https://www.jstor.org/stable/2988573 proxy for the beta distribution.]
 The distribution is used in business decision making and project planning.
 
 The [@http://en.wikipedia.org/wiki/Triangular_distribution triangular distribution]

diff --git a/doc/distributions/weibull.qbk b/doc/distributions/weibull.qbk
@@ -59,7 +59,7 @@ When ['[alpha]] = 1, the Weibull distribution reduces to the
 [@http://en.wikipedia.org/wiki/Exponential_distribution exponential distribution].
 The relationship of the types of extreme value distributions, of which the Weibull is but one, is
 discussed by
-[@http://www.worldscibooks.com/mathematics/p191.html Extreme Value Distributions, Theory and Applications
+[@https://www.google.com/books/edition/Extreme_Value_Distributions/GwBqDQAAQBAJ?hl=en&gbpv=0 Extreme Value Distributions, Theory and Applications
 Samuel Kotz & Saralees Nadarajah].
 
 

diff --git a/doc/graphs/fourier_transform_daubechies.png b/doc/graphs/fourier_transform_daubechies.png
diff --git a/doc/html/math_toolkit/dist_ref/dists/exp_dist.html b/doc/html/math_toolkit/dist_ref/dists/exp_dist.html
@@ -305,7 +305,7 @@ <h5>
           Distributions</a>.)
         </p>
 <div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem">
-              <a href="http://www.worldscibooks.com/mathematics/p191.html" target="_top">Extreme
+              <a href="https://www.google.com/books/edition/Extreme_Value_Distributions/GwBqDQAAQBAJ?hl=en&gbpv=0" target="_top">Extreme
               Value Distributions, Theory and Applications Samuel Kotz &amp; Saralees
               Nadarajah</a> discuss the relationship of the types of extreme
               value distributions.

diff --git a/doc/html/math_toolkit/dist_ref/dists/extreme_dist.html b/doc/html/math_toolkit/dist_ref/dists/extreme_dist.html
@@ -65,7 +65,7 @@
         </p>
 <p>
           The relationship of the types of extreme value distributions, of which
-          this is but one, is discussed by <a href="http://www.worldscibooks.com/mathematics/p191.html" target="_top">Extreme
+          this is but one, is discussed by <a href="https://www.google.com/books/edition/Extreme_Value_Distributions/GwBqDQAAQBAJ?hl=en&gbpv=0" target="_top">Extreme
           Value Distributions, Theory and Applications Samuel Kotz &amp; Saralees
           Nadarajah</a>.
         </p>

diff --git a/doc/html/math_toolkit/dist_ref/dists/triangular_dist.html b/doc/html/math_toolkit/dist_ref/dists/triangular_dist.html
@@ -64,7 +64,7 @@
           vaguely known, but, like the <a href="http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29" target="_top">uniform
           distribution</a>, upper and limits are 'known', but a 'best guess',
           the mode or center point, is also added. It has been recommended as a
-          <a href="http://www.worldscibooks.com/mathematics/etextbook/5720/5720_chap1.pdf" target="_top">proxy
+          <a href="https://www.jstor.org/stable/2988573" target="_top">proxy
           for the beta distribution.</a> The distribution is used in business
           decision making and project planning.
         </p>

diff --git a/doc/html/math_toolkit/dist_ref/dists/weibull_dist.html b/doc/html/math_toolkit/dist_ref/dists/weibull_dist.html
@@ -108,7 +108,7 @@ <h5>
           distribution</a>. When <span class="emphasis"><em>α</em></span> = 1, the Weibull distribution
           reduces to the <a href="http://en.wikipedia.org/wiki/Exponential_distribution" target="_top">exponential
           distribution</a>. The relationship of the types of extreme value distributions,
-          of which the Weibull is but one, is discussed by <a href="http://www.worldscibooks.com/mathematics/p191.html" target="_top">Extreme
+          of which the Weibull is but one, is discussed by <a href="https://www.google.com/books/edition/Extreme_Value_Distributions/GwBqDQAAQBAJ?hl=en&gbpv=0" target="_top">Extreme
           Value Distributions, Theory and Applications Samuel Kotz &amp; Saralees
           Nadarajah</a>.
         </p>

diff --git a/doc/html/math_toolkit/refs.html b/doc/html/math_toolkit/refs.html
@@ -78,7 +78,7 @@ <h5>
       by N.A.J. Hastings, Brian Peacock, Merran Evans, ISBN: 0471371246, Wiley 2000.
     </p>
 <p>
-      <a href="http://www.worldscibooks.com/mathematics/p191.html" target="_top">Extreme Value
+      <a href="https://www.google.com/books/edition/Extreme_Value_Distributions/GwBqDQAAQBAJ?hl=en&gbpv=0" target="_top">Extreme Value
       Distributions, Theory and Applications</a> Samuel Kotz &amp; Saralees Nadarajah,
       ISBN 978-1-86094-224-2 &amp; 1-86094-224-5 Oct 2000, Chapter 1.2 discusses
       the various extreme value distributions.

diff --git a/doc/math.qbk b/doc/math.qbk
@@ -615,6 +615,7 @@ and as a CD ISBN 0-9504833-2-X  978-0-9504833-2-0, Classification 519.2-dc22.
 [include statistics/runs_test.qbk]
 [include statistics/ljung_box.qbk]
 [include statistics/linear_regression.qbk]
+[include statistics/chatterjee_correlation.qbk]
 [endmathpart] [/section:statistics Statistics]
 
 [mathpart vector_functionals Vector Functionals -  Norms]

diff --git a/doc/sf/daubechies.qbk b/doc/sf/daubechies.qbk
@@ -127,6 +127,48 @@ The 2 vanishing moment scaling function.
 [$../graphs/daubechies_8_scaling.svg]
 The 8 vanishing moment scaling function.
 
+Boost.Math also provides numerical evaluation of the Fourier transform of these functions.
+This is useful in sparse recovery problems where the measurements are taken in the Fourier basis.
+The usage is exhibited below:
+
+    #include <boost/math/special_functions/fourier_transform_daubechies_scaling.hpp>
+    using boost::math::fourier_transform_daubechies_scaling;
+    // Evaluate the Fourier transform of the 4-vanishing moment Daubechies scaling function at ω=1.8:
+    std::complex<float> hat_phi = fourier_transform_daubechies_scaling<float, 4>(1.8f);
+
+The Fourier transform convention is unitary with the sign of the imaginary unit being given in Daubechies Ten Lectures.
+In particular, this means that `fourier_transform_daubechies_scaling<float, p>(0.0)` returns 1/sqrt(2π).
+
+The implementation computes an infinite product of trigonometric polynomials as can be found from recursive application of the identity 𝓕[φ](ω) = m(ω/2)𝓕[φ](ω/2).
+This is neither particularly fast nor accurate, but there appears to be no literature on this extremely useful topic, and hence the naive method must suffice.
+
+[$../graphs/fourier_transform_daubechies.png]
+
+A benchmark can be found in `reporting/performance/fourier_transform_daubechies_performance.cpp`; the results on a ~2021 M1 Macbook pro are presented below:
+
+
+Run on (10 X 24.1212 MHz CPU s)
+CPU Caches:
+  L1 Data 64 KiB (x10)
+  L1 Instruction 128 KiB (x10)
+  L2 Unified 4096 KiB (x5)
+Load Average: 1.33, 1.52, 1.62
+-----------------------------------------------------------
+Benchmark                                              Time 
+-----------------------------------------------------------
+FourierTransformDaubechiesScaling<double, 1>        70.3 ns
+FourierTransformDaubechiesScaling<double, 2>         330 ns
+FourierTransformDaubechiesScaling<double, 3>         335 ns
+FourierTransformDaubechiesScaling<double, 4>         364 ns
+FourierTransformDaubechiesScaling<double, 5>         386 ns
+FourierTransformDaubechiesScaling<double, 6>         436 ns
+FourierTransformDaubechiesScaling<double, 7>         447 ns
+FourierTransformDaubechiesScaling<double, 8>         473 ns
+FourierTransformDaubechiesScaling<double, 9>         503 ns
+FourierTransformDaubechiesScaling<double, 10>        554 ns
+
+Due to the low accuracy of this method, `float` precision is arg-promoted to `double`, and hence takes just as long as `double` precision to execute.
+
 [heading References]
 
 * Daubechies, Ingrid. ['Ten Lectures on Wavelets.] Vol. 61. Siam, 1992.

diff --git a/example/calculate_fourier_transform_daubechies_constants.cpp b/example/calculate_fourier_transform_daubechies_constants.cpp
@@ -0,0 +1,43 @@
+//  (C) Copyright Nick Thompson 2023.
+//  Use, modification and distribution are subject to the
+//  Boost Software License, Version 1.0. (See accompanying file
+//  LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
+
+#include <utility>
+#include <boost/math/filters/daubechies.hpp>
+#include <boost/math/tools/polynomial.hpp>
+#include <boost/multiprecision/cpp_bin_float.hpp>
+#include <boost/math/constants/constants.hpp>
+
+using std::pow;
+using boost::multiprecision::cpp_bin_float_100;
+using boost::math::filters::daubechies_scaling_filter;
+using boost::math::tools::polynomial;
+using boost::math::constants::half;
+using boost::math::constants::root_two;
+
+template<typename Real, size_t N>
+std::vector<Real> get_constants() {
+   auto h = daubechies_scaling_filter<cpp_bin_float_100, N>();
+   auto p = polynomial<cpp_bin_float_100>(h.begin(), h.end());
+
+   auto q = polynomial({half<cpp_bin_float_100>(), half<cpp_bin_float_100>()});
+   q = pow(q, N);
+   auto l = p/q;
+   return l.data(); 
+}
+
+template<typename Real>
+void print_constants(std::vector<Real> const & l) {
+   std::cout << std::setprecision(std::numeric_limits<Real>::digits10 -10);
+   std::cout << "return std::array<Real, " << l.size() << ">{";
+   for (size_t i = 0; i < l.size() - 1; ++i) {
+       std::cout << "BOOST_MATH_BIG_CONSTANT(Real, std::numeric_limits<Real>::digits, " << l[i]/root_two<Real>() << "), ";
+   }
+   std::cout << "BOOST_MATH_BIG_CONSTANT(Real, std::numeric_limits<Real>::digits, " << l.back()/root_two<Real>() << ")};\n";
+}
+
+int main() {
+   auto constants = get_constants<cpp_bin_float_100, 1>();
+   print_constants(constants);
+}