Add NEWS.md, fix some bugs

NEWS.md

@@ -1,4 +1,7 @@
+## Groebner v0.4.0 Release notes 
 
-Changelog
-
+Minor bug fixes and improvements.
 
+Changes in the interface:
+- Keyword argument `rng` removed. Instead, now the `seed` keyword argument is provided for setting the seed of the internal random number generator.
+- Changed keyword arguments `linalg`, `monoms`, and `loglevel`.

README.md

@@ -3,23 +3,24 @@
 [![Runtests](https://github.com/sumiya11/Groebner.jl/actions/workflows/Runtests.yml/badge.svg)](https://github.com/sumiya11/Groebner.jl/actions/workflows/Runtests.yml)
 [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://sumiya11.github.io/Groebner.jl)
 
+Groebner.jl is a Julia package for computing Groebner bases over fields.
 
-The package provides Groebner bases computation interface in pure Julia with the performance comparable to Singular.
-`Groebner.jl` works over finite fields and over the rationals, and supports various monomial orderings.
+For documentation and more please check out https://sumiya11.github.io/Groebner.jl.
+For a simple example, see below.
 
-For documentation and more please check out https://sumiya11.github.io/Groebner.jl
-
-Groebner.jl can be extended : how the code is structured .
+## How to use Groebner.jl?
 
-# TODO: say how one can contribute.
+You can install Groebner.jl using the Julia package manager. From the Julia REPL, type
 
-## How to use Groebner.jl?
+```julia
+import Pkg; Pkg.add("Groebner")
+```
 
-Our package works with polynomials from `AbstractAlgebra.jl`, `DynamicPolynomials.jl`, and `Nemo.jl`. We will demonstrate the usage on a simple example. Lets first create a ring of polynomials in 3 variables
+Our package works with polynomials from `AbstractAlgebra.jl`, `DynamicPolynomials.jl`, and `Nemo.jl`. Let's create a ring of polynomials in 3 variables
 
 ```julia
 using AbstractAlgebra
-R, (x1, x2, x3) = PolynomialRing(QQ, ["x1", "x2", "x3"]);
+R, (x1, x2, x3) = PolynomialRing(QQ, ["x1", "x2", "x3"])
 ```
 
 Then we can define a simple polynomial system
@@ -29,11 +30,10 @@ polys = [
   x1 + x2 + x3,
   x1*x2 + x1*x3 + x2*x3,
   x1*x2*x3 - 1
-];
+]
 ```
 
-And compute the Groebner basis passing the system to `groebner`
-
+And compute the Groebner basis by passing the system to `groebner`
 
 ```julia
 using Groebner

experimental/SI/fujita.jl → experimental/SI/si.jl

@@ -3,12 +3,10 @@ using AbstractAlgebra, BenchmarkTools, JLD2
 import Nemo, Profile
 
 macro myprof(ex)
-    :(
-        (VSCodeServer.Profile).clear();
-        Profile.init(n=10^7, delay=0.0000001);
-        Profile.@profile $ex;
-        VSCodeServer.view_profile(;)
-    )
+    :((VSCodeServer.Profile).clear();
+    Profile.init(n=10^7, delay=0.0000001);
+    Profile.@profile $ex;
+    VSCodeServer.view_profile(;))
 end
 
 ode = @ODEmodel(
@@ -44,6 +42,14 @@ ode = @ODEmodel(
     y3(t) = a3 * pS6(t)
 )
 
+siwr = @ODEmodel(
+    S'(t) = mu - bi * S(t) * I(t) - bw * S(t) * W(t) - mu * S(t) + a * R(t),
+    I'(t) = bw * S(t) * W(t) + bi * S(t) * I(t) - (gam + mu) * I(t),
+    W'(t) = xi * (I(t) - W(t)),
+    R'(t) = gam * I(t) - (mu + a) * R(t),
+    y(t) = k * I(t)
+)
+
 G = StructuralIdentifiability.ideal_generators(ode);
 
 par = parent(G[1])
@@ -67,7 +73,7 @@ Groebner.logging_enabled() = false
 Groebner.invariants_enabled() = false
 
 gb = Groebner.groebner(G_zp);
-graph, gb_1 = Groebner.groebner_learn(G_zp);
+@time graph, gb_1 = Groebner.groebner_learn(G_zp);
 graph
 graph.matrix_infos
 flag, gb_2 = Groebner.groebner_apply!(graph, G_zp)
@@ -77,8 +83,7 @@ flag, gb_2 = Groebner.groebner_apply!(graph, G_zp)
 @btime Groebner.groebner_apply!($graph, $G_zp);
 
 @myprof begin
-    for i in 1:1000
+    for i in 1:100
         Groebner.groebner_apply!(graph, G_zp)
     end
 end
-

src/Groebner.jl

@@ -20,17 +20,15 @@ invariants_enabled() = true
 Specifies if custom logging is enabled.
 If `false`, then all logging is disabled, and entails no runtime overhead.
 
-See also `@log` in `src/uti0-w2q3=-asqqw2q=[-edrfls/logging.jl`.
+See also `@log` in `src/utils/logging.jl`.
 """
 logging_enabled() = true
 
-# Groebner does not provide
-# a polynomial implementation of its own but relies on existing symbolic
-# computation packages in Julia for communicating with the user instead.
-# Groebner accepts as its input polynomials from the Julia packages
-# AbstractAlgebra.jl (Oscar.jl) and MultivariatePolynomials.jl. This list can be
-# extended; see `src/input-output.jl` for details. TODO: make the
-# AbstractAlgebra and MultivariatePolynomials dependencies optional!
+# Groebner does not provide a polynomial implementation of its own but relies on
+# existing symbolic computation packages in Julia for communicating with the
+# user instead. Groebner accepts as its input polynomials from the Julia
+# packages AbstractAlgebra.jl (Oscar.jl) and MultivariatePolynomials.jl. This
+# list can be extended; see `src/input-output/` for details.
 import AbstractAlgebra
 import AbstractAlgebra: base_ring, elem_type
 
@@ -78,7 +76,7 @@ include("utils/parameters.jl")
 # Input-output conversions for polynomials
 include("input-output/input-output.jl")
 include("input-output/AbstractAlgebra.jl")
-# include("input-output/DynamicPolynomials.jl")
+include("input-output/DynamicPolynomials.jl")
 
 #= generic f4 implementation =#
 #= the heart of this library =#

src/arithmetic/Zp.jl

@@ -64,14 +64,8 @@ end
 end
 
 
-# finite field arithmetic in case of UInt8, UInt16, UInt32, UInt64
 function select_arithmetic(coeffs::Vector{Vector{T}}, ch) where {T <: CoeffFF}
-    SpecializedBuiltinArithmeticZp(unsigned(ch))
-end
-
-# finite field arithmetic in case of UInt128
-function select_arithmetic(coeffs::Vector{Vector{UInt128}}, ch)
-    SpecializedBuiltinArithmeticZp(unsigned(ch))
+    SpecializedBuiltinArithmeticZp(convert(T, ch))
 end
 
 # arithmetic over rational numbers