Added multidimensional Rosenbrock derivative and Hessian

argmin/src/core/checkpointing/mod.rs

@@ -35,7 +35,7 @@
 //! use argmin_checkpointing_file::FileCheckpoint;
 //! # use argmin_observer_slog::SlogLogger;
 //! # use argmin::solver::landweber::Landweber;
-//! # use argmin_testfunctions::{rosenbrock_2d, rosenbrock_2d_derivative};
+//! # use argmin_testfunctions::{rosenbrock, rosenbrock_derivative};
 //! #
 //! # #[derive(Default)]
 //! # struct Rosenbrock {}
@@ -49,7 +49,7 @@
 //! #
 //! #     /// Apply the cost function to a parameter `p`
 //! #     fn cost(&self, p: &Self::Param) -> Result<Self::Output, Error> {
-//! #         Ok(rosenbrock_2d(&[p[0], p[1]], 1.0, 100.0))
+//! #         Ok(rosenbrock(p, 1.0, 100.0))
 //! #     }
 //! # }
 //! #
@@ -62,7 +62,7 @@
 //! #
 //! #     /// Compute the gradient at parameter `p`.
 //! #     fn gradient(&self, p: &Self::Param) -> Result<Self::Gradient, Error> {
-//! #         Ok(rosenbrock_2d_derivative(&[p[0], p[1]], 1.0, 100.0).to_vec())
+//! #         Ok(rosenbrock_derivative(p, 1.0, 100.0))
 //! #     }
 //! # }
 //! #

argmin/src/core/observers/mod.rs

@@ -36,7 +36,7 @@
 //! # use argmin_observer_paramwriter::{ParamWriter, ParamWriterFormat};
 //! # use argmin::solver::gradientdescent::SteepestDescent;
 //! # use argmin::solver::linesearch::MoreThuenteLineSearch;
-//! # use argmin_testfunctions::{rosenbrock_2d, rosenbrock_2d_derivative};
+//! # use argmin_testfunctions::{rosenbrock, rosenbrock_derivative};
 //! #
 //! # struct Rosenbrock {
 //! #     a: f64,
@@ -52,7 +52,7 @@
 //! #
 //! #     /// Apply the cost function to a parameter `p`
 //! #     fn cost(&self, p: &Self::Param) -> Result<Self::Output, Error> {
-//! #         Ok(rosenbrock_2d(&[p[0], p[1]], 1.0, 100.0))
+//! #         Ok(rosenbrock(p, 1.0, 100.0))
 //! #     }
 //! # }
 //! #
@@ -65,7 +65,7 @@
 //! #
 //! #     /// Compute the gradient at parameter `p`.
 //! #     fn gradient(&self, p: &Self::Param) -> Result<Self::Gradient, Error> {
-//! #         Ok(rosenbrock_2d_derivative(&[p[0], p[1]], 1.0, 100.0).to_vec())
+//! #         Ok(rosenbrock_derivative(p, 1.0, 100.0))
 //! #     }
 //! # }
 //! #

examples/checkpoint/src/main.rs

@@ -14,7 +14,7 @@ use argmin::{
 };
 use argmin_checkpointing_file::FileCheckpoint;
 use argmin_observer_slog::SlogLogger;
-use argmin_testfunctions::{rosenbrock_2d, rosenbrock_2d_derivative};
+use argmin_testfunctions::{rosenbrock, rosenbrock_derivative};
 
 #[derive(Default)]
 struct Rosenbrock {}
@@ -24,7 +24,7 @@ impl CostFunction for Rosenbrock {
     type Output = f64;
 
     fn cost(&self, p: &Self::Param) -> Result<Self::Output, Error> {
-        Ok(rosenbrock_2d(&[p[0], p[1]], 1.0, 100.0))
+        Ok(rosenbrock(p, 1.0, 100.0))
     }
 }
 
@@ -33,7 +33,7 @@ impl Gradient for Rosenbrock {
     type Gradient = Vec<f64>;
 
     fn gradient(&self, p: &Self::Param) -> Result<Self::Gradient, Error> {
-        Ok(rosenbrock_2d_derivative(&[p[0], p[1]], 1.0, 100.0).to_vec())
+        Ok(rosenbrock_derivative(p, 1.0, 100.0))
     }
 }
 

examples/landweber/src/main.rs

@@ -10,7 +10,7 @@ use argmin::{
     solver::landweber::Landweber,
 };
 use argmin_observer_slog::SlogLogger;
-use argmin_testfunctions::rosenbrock_2d_derivative;
+use argmin_testfunctions::rosenbrock_derivative;
 
 struct Rosenbrock {}
 
@@ -19,7 +19,7 @@ impl Gradient for Rosenbrock {
     type Gradient = Vec<f64>;
 
     fn gradient(&self, p: &Self::Param) -> Result<Self::Gradient, Error> {
-        Ok(rosenbrock_2d_derivative(&[p[0], p[1]], 1.0, 100.0).to_vec())
+        Ok(rosenbrock_derivative(p, 1.0, 100.0))
     }
 }
 

examples/lbfgs/src/main.rs

@@ -10,8 +10,7 @@ use argmin::{
     solver::{linesearch::MoreThuenteLineSearch, quasinewton::LBFGS},
 };
 use argmin_observer_slog::SlogLogger;
-use argmin_testfunctions::rosenbrock;
-use finitediff::FiniteDiff;
+use argmin_testfunctions::{rosenbrock, rosenbrock_derivative};
 use ndarray::{array, Array1};
 
 struct Rosenbrock {
@@ -24,15 +23,19 @@ impl CostFunction for Rosenbrock {
     type Output = f64;
 
     fn cost(&self, p: &Self::Param) -> Result<Self::Output, Error> {
-        Ok(rosenbrock(&p.to_vec(), self.a, self.b))
+        Ok(rosenbrock(p.as_slice().unwrap(), self.a, self.b))
     }
 }
 impl Gradient for Rosenbrock {
     type Param = Array1<f64>;
     type Gradient = Array1<f64>;
 
     fn gradient(&self, p: &Self::Param) -> Result<Self::Gradient, Error> {
-        Ok((*p).forward_diff(&|x| rosenbrock(&x.to_vec(), self.a, self.b)))
+        Ok(Array1::from(rosenbrock_derivative(
+            p.as_slice().unwrap(),
+            self.a,
+            self.b,
+        )))
     }
 }
 
@@ -42,7 +45,7 @@ fn run() -> Result<(), Error> {
 
     // Define initial parameter vector
     let init_param: Array1<f64> = array![-1.2, 1.0];
-    // let init_param: Array1<f64> = array![-1.2, 1.0, -10.0, 2.0, 3.0, 2.0, 4.0, 10.0];
+    // let init_param: Array1<f64> = array![-1.2, 1.0, -10.0, 2.0, 3.0, 2.0, 4.0, 9.0];
 
     // set up a line search
     let linesearch = MoreThuenteLineSearch::new().with_c(1e-4, 0.9)?;

examples/lbfgs_nalgebra/src/main.rs

@@ -10,7 +10,7 @@ use argmin::{
     solver::{linesearch::MoreThuenteLineSearch, quasinewton::LBFGS},
 };
 use argmin_observer_slog::SlogLogger;
-use argmin_testfunctions::{rosenbrock_2d, rosenbrock_2d_derivative};
+use argmin_testfunctions::{rosenbrock, rosenbrock_derivative};
 use nalgebra::DVector;
 
 struct Rosenbrock {
@@ -23,7 +23,7 @@ impl CostFunction for Rosenbrock {
     type Output = f64;
 
     fn cost(&self, p: &Self::Param) -> Result<Self::Output, Error> {
-        Ok(rosenbrock_2d(&[p[0], p[1]], self.a, self.b))
+        Ok(rosenbrock(p.as_slice(), self.a, self.b))
     }
 }
 
@@ -32,9 +32,11 @@ impl Gradient for Rosenbrock {
     type Gradient = DVector<f64>;
 
     fn gradient(&self, p: &Self::Param) -> Result<Self::Gradient, Error> {
-        Ok(DVector::from(
-            rosenbrock_2d_derivative(&[p[0], p[1]], self.a, self.b).to_vec(),
-        ))
+        Ok(DVector::from(rosenbrock_derivative(
+            p.as_slice(),
+            self.a,
+            self.b,
+        )))
     }
 }
 

examples/newton/src/main.rs

@@ -10,7 +10,7 @@ use argmin::{
     solver::newton::Newton,
 };
 use argmin_observer_slog::SlogLogger;
-use argmin_testfunctions::{rosenbrock_2d_derivative, rosenbrock_2d_hessian};
+use argmin_testfunctions::{rosenbrock_derivative, rosenbrock_hessian};
 use ndarray::{Array, Array1, Array2};
 
 struct Rosenbrock {
@@ -23,9 +23,11 @@ impl Gradient for Rosenbrock {
     type Gradient = Array1<f64>;
 
     fn gradient(&self, p: &Self::Param) -> Result<Self::Gradient, Error> {
-        Ok(Array1::from(
-            rosenbrock_2d_derivative(&[p[0], p[1]], self.a, self.b).to_vec(),
-        ))
+        Ok(Array1::from(rosenbrock_derivative(
+            p.as_slice().unwrap(),
+            self.a,
+            self.b,
+        )))
     }
 }
 
@@ -34,12 +36,11 @@ impl Hessian for Rosenbrock {
     type Hessian = Array2<f64>;
 
     fn hessian(&self, p: &Self::Param) -> Result<Self::Hessian, Error> {
-        let h = rosenbrock_2d_hessian(&[p[0], p[1]], self.a, self.b)
-            .iter()
+        let h = rosenbrock_hessian(p.as_slice().unwrap(), self.a, self.b)
+            .into_iter()
             .flatten()
-            .cloned()
             .collect();
-        Ok(Array::from_shape_vec((2, 2), h)?)
+        Ok(Array::from_shape_vec((p.len(), p.len()), h)?)
     }
 }
 
@@ -48,15 +49,14 @@ fn run() -> Result<(), Error> {
     let cost = Rosenbrock { a: 1.0, b: 100.0 };
 
     // Define initial parameter vector
-    // let init_param: Array1<f64> = Array1::from(vec![1.2, 1.2]);
-    let init_param: Array1<f64> = Array1::from(vec![-1.2, 1.0]);
+    let init_param: Array1<f64> = Array1::from(vec![1.2, 1.2]);
 
     // Set up solver
     let solver: Newton<f64> = Newton::new();
 
     // Run solver
     let res = Executor::new(cost, solver)
-        .configure(|state| state.param(init_param).max_iters(8))
+        .configure(|state| state.param(init_param).max_iters(10))
         .add_observer(SlogLogger::term(), ObserverMode::Always)
         .run()?;
 

examples/newton_cg/src/main.rs

@@ -10,7 +10,7 @@ use argmin::{
     solver::{linesearch::MoreThuenteLineSearch, newton::NewtonCG},
 };
 use argmin_observer_slog::SlogLogger;
-use argmin_testfunctions::{rosenbrock_2d, rosenbrock_2d_derivative, rosenbrock_2d_hessian};
+use argmin_testfunctions::{rosenbrock, rosenbrock_derivative, rosenbrock_hessian};
 use ndarray::{Array, Array1, Array2};
 
 struct Rosenbrock {
@@ -23,7 +23,7 @@ impl CostFunction for Rosenbrock {
     type Output = f64;
 
     fn cost(&self, p: &Self::Param) -> Result<Self::Output, Error> {
-        Ok(rosenbrock_2d(&[p[0], p[1]], self.a, self.b))
+        Ok(rosenbrock(p.as_slice().unwrap(), self.a, self.b))
     }
 }
 
@@ -33,7 +33,7 @@ impl Gradient for Rosenbrock {
 
     fn gradient(&self, p: &Self::Param) -> Result<Self::Gradient, Error> {
         Ok(Array1::from(
-            rosenbrock_2d_derivative(&[p[0], p[1]], self.a, self.b).to_vec(),
+            rosenbrock_derivative(p.as_slice().unwrap(), self.a, self.b).to_vec(),
         ))
     }
 }
@@ -43,12 +43,11 @@ impl Hessian for Rosenbrock {
     type Hessian = Array2<f64>;
 
     fn hessian(&self, p: &Self::Param) -> Result<Self::Hessian, Error> {
-        let h = rosenbrock_2d_hessian(&[p[0], p[1]], self.a, self.b)
-            .iter()
+        let h = rosenbrock_hessian(p.as_slice().unwrap(), self.a, self.b)
+            .into_iter()
             .flatten()
-            .cloned()
             .collect();
-        Ok(Array::from_shape_vec((2, 2), h)?)
+        Ok(Array::from_shape_vec((p.len(), p.len()), h)?)
     }
 }
 
@@ -57,7 +56,6 @@ fn run() -> Result<(), Error> {
     let cost = Rosenbrock { a: 1.0, b: 100.0 };
 
     // Define initial parameter vector
-    // let init_param: Array1<f64> = Array1::from(vec![1.2, 1.2]);
     let init_param: Array1<f64> = Array1::from(vec![-1.2, 1.0]);
 
     // set up line search

examples/nonlinear_cg/src/main.rs

@@ -13,7 +13,7 @@ use argmin::{
     },
 };
 use argmin_observer_slog::SlogLogger;
-use argmin_testfunctions::{rosenbrock_2d, rosenbrock_2d_derivative};
+use argmin_testfunctions::{rosenbrock, rosenbrock_derivative};
 
 struct Rosenbrock {}
 
@@ -22,7 +22,7 @@ impl CostFunction for Rosenbrock {
     type Output = f64;
 
     fn cost(&self, p: &Self::Param) -> Result<Self::Output, Error> {
-        Ok(rosenbrock_2d(&[p[0], p[1]], 1.0, 100.0))
+        Ok(rosenbrock(p, 1.0, 100.0))
     }
 }
 
@@ -31,7 +31,7 @@ impl Gradient for Rosenbrock {
     type Gradient = Vec<f64>;
 
     fn gradient(&self, p: &Self::Param) -> Result<Self::Gradient, Error> {
-        Ok(rosenbrock_2d_derivative(&[p[0], p[1]], 1.0, 100.0).to_vec())
+        Ok(rosenbrock_derivative(p, 1.0, 100.0))
     }
 }
 

examples/paramwriter/src/main.rs

@@ -10,7 +10,7 @@ use argmin::{
     solver::{gradientdescent::SteepestDescent, linesearch::MoreThuenteLineSearch},
 };
 use argmin_observer_paramwriter::{ParamWriter, ParamWriterFormat};
-use argmin_testfunctions::{rosenbrock_2d, rosenbrock_2d_derivative};
+use argmin_testfunctions::{rosenbrock, rosenbrock_derivative};
 
 struct Rosenbrock {
     a: f64,
@@ -22,7 +22,7 @@ impl CostFunction for Rosenbrock {
     type Output = f64;
 
     fn cost(&self, p: &Self::Param) -> Result<Self::Output, Error> {
-        Ok(rosenbrock_2d(&[p[0], p[1]], self.a, self.b))
+        Ok(rosenbrock(p, self.a, self.b))
     }
 }
 
@@ -31,7 +31,7 @@ impl Gradient for Rosenbrock {
     type Gradient = Vec<f64>;
 
     fn gradient(&self, p: &Self::Param) -> Result<Self::Gradient, Error> {
-        Ok(rosenbrock_2d_derivative(&[p[0], p[1]], self.a, self.b).to_vec())
+        Ok(rosenbrock_derivative(p, self.a, self.b))
     }
 }
 

examples/steepestdescent/src/main.rs

@@ -14,7 +14,7 @@ use argmin::{
     },
 };
 use argmin_observer_slog::SlogLogger;
-use argmin_testfunctions::{rosenbrock_2d, rosenbrock_2d_derivative};
+use argmin_testfunctions::{rosenbrock, rosenbrock_derivative};
 
 struct Rosenbrock {
     a: f64,
@@ -26,7 +26,7 @@ impl CostFunction for Rosenbrock {
     type Output = f64;
 
     fn cost(&self, p: &Self::Param) -> Result<Self::Output, Error> {
-        Ok(rosenbrock_2d(&[p[0], p[1]], self.a, self.b))
+        Ok(rosenbrock(p, self.a, self.b))
     }
 }
 
@@ -35,7 +35,7 @@ impl Gradient for Rosenbrock {
     type Gradient = Vec<f64>;
 
     fn gradient(&self, p: &Self::Param) -> Result<Self::Gradient, Error> {
-        Ok(rosenbrock_2d_derivative(&[p[0], p[1]], self.a, self.b).to_vec())
+        Ok(rosenbrock_derivative(p, self.a, self.b))
     }
 }
 
@@ -58,7 +58,7 @@ fn run() -> Result<(), Error> {
 
     // Run solver
     let res = Executor::new(cost, solver)
-        .configure(|state| state.param(init_param).max_iters(10))
+        .configure(|state| state.param(init_param).max_iters(100))
         .add_observer(SlogLogger::term(), ObserverMode::Always)
         .run()?;
 

examples/trustregion/Cargo.toml

@@ -7,7 +7,8 @@ publish = false
 
 [dependencies]
 argmin = { version = "*", path = "../../argmin" }
-argmin-math = { version = "*", features = ["ndarray_latest-nolinalg"], path = "../../argmin-math" }
+argmin-math = { version = "*", features = ["ndarray_latest"], path = "../../argmin-math" }
 argmin-observer-slog = { version = "*", path = "../../observers/slog/" }
 argmin_testfunctions = { version = "*", path = "../../tools/testfunctions/" }
 ndarray = "0.15.6"
+ndarray-linalg = { version = "0.16.0", features = ["intel-mkl-static"] }