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// Copyright 2018 Stefan Kroboth // // Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or // http://apache.org/licenses/LICENSE-2.0> or the MIT license <LICENSE-MIT or // http://opensource.org/licenses/MIT>, at your option. This file may not be // copied, modified, or distributed except according to those terms. //! A pure Rust optimization framework //! //! This crate offers a (work in progress) numerical optimization toolbox/framework written entirely //! in Rust. It is at the moment quite unstable and potentially very buggy. Please use with care and //! report any bugs you encounter. This crate is looking for contributors! //! //! # Design goals //! //! This crate's intention is to be useful to users as well as developers of optimization //! algorithms, meaning that it should be both easy to apply and easy to implement algorithms. In //! particular, as a developer of optimization algorithms you should not need to worry about //! usability features (such as logging, dealing with different types, setters and getters for //! certain common parameters, counting cost function and gradient evaluations, termination, and so //! on). Instead you can focus on implementing your algorithm and let `argmin-codegen` do the rest. //! //! - Easy framework for the implementation of optimization algorithms: Define a struct to hold your //! data, implement a single iteration of your method and let argmin generate the rest with //! `#[derive(ArgminSolver)]`. This lead to similar interfaces for different solvers, making it //! easy for users. //! - Pure Rust implementations of a wide range of optimization methods: This avoids the need to //! compile and interface C/C++/Fortran code. //! - Type-agnostic: Many problems require data structures that go beyond simple vectors to //! represent the parameters. In argmin, everything is generic: All that needs to be done is //! implementing certain traits on your data type. For common types, these traits are already //! implemented. //! - Convenient: Automatic and consistent logging of anything that may be important. Log to the //! terminal, to a file or implement your own loggers. Future plans include sending metrics to //! databases and connecting to big data piplines. //! - Algorithm evaluation: Methods to assess the performance of an algorithm for different //! parameter settings, problem classes, ... //! //! Since this crate is in a very early stage, so far most points are only partially implemented or //! remain future plans. //! //! # Algorithms //! //! - [Line searches](solver/linesearch/index.html) //! - [Backtracking line search](solver/linesearch/backtracking/struct.BacktrackingLineSearch.html) //! - [More-Thuente line search](solver/linesearch/morethuente/struct.MoreThuenteLineSearch.html) //! - [Hager-Zhang line search](solver/linesearch/hagerzhang/struct.HagerZhangLineSearch.html) //! - [Trust region method](solver/trustregion/trustregion_method/struct.TrustRegion.html) //! - [Cauchy point method](solver/trustregion/cauchypoint/struct.CauchyPoint.html) //! - [Dogleg method](solver/trustregion/dogleg/struct.Dogleg.html) //! - [Steihaug method](solver/trustregion/steihaug/struct.Steihaug.html) //! - [Steepest descent](solver/gradientdescent/steepestdescent/struct.SteepestDescent.html) //! - [Conjugate gradient method](solver/conjugategradient/cg/struct.ConjugateGradient.html) //! - [Nonlinear conjugate gradient method](solver/conjugategradient/nonlinear_cg/struct.NonlinearConjugateGradient.html) //! - [Newton methods](solver/newton/index.html) //! - [Newton's method](solver/newton/newton_method/struct.Newton.html) //! - [Newton-CG](solver/newton/newton_cg/struct.NewtonCG.html) //! - [Quasi-Newton methods](solver/quasinewton/index.html) //! - [BFGS](solver/quasinewton/bfgs/struct.BFGS.html) //! - [Landweber iteration](solver/landweber/struct.Landweber.html) //! - [Simulated Annealing](solver/simulatedannealing/struct.SimulatedAnnealing.html) //! //! # Usage //! //! Add this to your `Cargo.toml`: //! //! ```toml //! [dependencies] //! argmin = "0.1.8" //! ``` //! //! ## Optional features //! //! There are additional features which can be activated in `Cargo.toml`: //! //! ```toml //! [dependencies] //! argmin = { version = "0.1.8", features = ["ctrlc", "ndarrayl"] } //! ``` //! //! These may become default features in the future. Without these features compilation to //! `wasm32-unknown-unkown` seems to be possible. //! //! - `ctrlc`: Uses the `ctrlc` crate to properly stop the optimization (and return the current best //! result) after pressing Ctrl+C. //! - `ndarrayl`: Support for `ndarray` and `ndarray-linalg`. //! //! # Defining a problem //! //! A problem can be defined by implementing the `ArgminOp` trait which comes with the //! associated types `Param`, `Output` and `Hessian`. `Param` is the type of your //! parameter vector (i.e. the input to your cost function), `Output` is the type returned //! by the cost function and `Hessian` is the type of the Hessian. //! The trait provides the following methods: //! //! - `apply(&self, p: &Self::Param) -> Result<Self::Output, Error>`: Applys the cost //! function to parameters `p` of type `Self::Param` and returns the cost function value. //! - `gradient(&self, p: &Self::Param) -> Result<Self::Param, Error>`: Computes the //! gradient at `p`. Optional. By default returns an `Err` if not implemented. //! - `hessian(&self, p: &Self::Param) -> Result<Self::Hessian, Error>`: Computes the Hessian //! at `p`. Optional. By default returns an `Err` if not implemented. The type of `Hessian` can //! be set to `()` if this method is not implemented. //! //! //! The following code snippet shows an example of how to use the Rosenbrock test functions from //! `argmin-testfunctions` in argmin: //! //! ```rust //! # extern crate argmin; //! # extern crate argmin_testfunctions; //! # extern crate ndarray; //! # use argmin::testfunctions::{rosenbrock_2d, rosenbrock_2d_derivative, rosenbrock_2d_hessian}; //! # use argmin::prelude::*; //! # use serde::{Serialize, Deserialize}; //! // [Imports omited] //! //! /// First, create a struct for your problem //! #[derive(Clone, Default, Serialize, Deserialize)] //! struct Rosenbrock { //! a: f64, //! b: f64, //! } //! //! /// Implement `ArgminOp` for `Rosenbrock` //! impl ArgminOp for Rosenbrock { //! /// Type of the parameter vector //! type Param = ndarray::Array1<f64>; //! /// Type of the return value computed by the cost function //! type Output = f64; //! /// Type of the Hessian. If no Hessian is available or needed for the used solver, this can //! /// be set to `()` //! type Hessian = ndarray::Array2<f64>; //! //! /// Apply the cost function to a parameter `p` //! fn apply(&self, p: &Self::Param) -> Result<Self::Output, Error> { //! Ok(rosenbrock_2d(&p.to_vec(), self.a, self.b)) //! } //! //! /// Compute the gradient at parameter `p`. This is optional: If not implemented, this //! /// method will return an `Err` when called. //! fn gradient(&self, p: &Self::Param) -> Result<Self::Param, Error> { //! Ok(ndarray::Array1::from_vec(rosenbrock_2d_derivative(&p.to_vec(), self.a, self.b))) //! } //! //! /// Compute the Hessian at parameter `p`. This is optional: If not implemented, this method //! /// will return an `Err` when called. //! fn hessian(&self, p: &Self::Param) -> Result<Self::Hessian, Error> { //! let h = rosenbrock_2d_hessian(&p.to_vec(), self.a, self.b); //! Ok(ndarray::Array::from_shape_vec((2, 2), h).unwrap()) //! } //! } //! ``` //! //! # Running a solver //! //! The following example shows how to use the previously shown definition of a problem in a //! Steepest Descent (Gradient Descent) solver. //! //! ``` //! extern crate argmin; //! extern crate ndarray; //! use argmin::testfunctions::{rosenbrock_2d, rosenbrock_2d_derivative, rosenbrock_2d_hessian}; //! use argmin::prelude::*; //! use argmin::solver::gradientdescent::SteepestDescent; //! use argmin::solver::linesearch::MoreThuenteLineSearch; //! use serde::{Serialize, Deserialize}; //! //! #[derive(Clone, Default, Serialize, Deserialize)] //! struct Rosenbrock { //! a: f64, //! b: f64, //! } //! //! impl ArgminOp for Rosenbrock { //! type Param = ndarray::Array1<f64>; //! type Output = f64; //! type Hessian = (); //! //! fn apply(&self, p: &Self::Param) -> Result<Self::Output, Error> { //! Ok(rosenbrock_2d(&p.to_vec(), self.a, self.b)) //! } //! //! fn gradient(&self, p: &Self::Param) -> Result<Self::Param, Error> { //! Ok(ndarray::Array1::from_vec(rosenbrock_2d_derivative(&p.to_vec(), self.a, self.b))) //! } //! } //! //! fn run() -> Result<(), Error> { //! // Define cost function //! let cost = Rosenbrock { a: 1.0, b: 100.0 }; //! //! // Define inital parameter vector //! let init_param = ndarray::Array1::from_vec(vec![-1.2, 1.0]); //! //! // Pick a line search. //! // let linesearch = HagerZhangLineSearch::new(cost.clone()); //! let linesearch = MoreThuenteLineSearch::new(cost.clone()); //! // let linesearch = BacktrackingLineSearch::new(cost.clone()); //! //! // Create solver //! let mut solver = SteepestDescent::new(cost, init_param, linesearch)?; //! //! // Set the maximum number of iterations to 1000 //! solver.set_max_iters(1000); //! //! // Attach a terminal logger (slog) to the solver //! solver.add_logger(ArgminSlogLogger::term()); //! //! // Run the solver //! solver.run()?; //! //! // Print the result //! println!("{:?}", solver.result()); //! Ok(()) //! } //! //! fn main() { //! if let Err(ref e) = run() { //! println!("{} {}", e.as_fail(), e.backtrace()); //! std::process::exit(1); //! } //! } //! ``` //! //! Executing `solver.run()?` performs the actual optimization. In addition, there is //! `solver.run_fast()?`, which only executes the optimization algorithm and avoids all convenience //! functionality such as logging. //! //! # Logging //! //! Information such as the current iteration number, cost function value, and other metrics can be //! logged using any object which implements `argmin_core::ArgminLogger`. So far loggers based on //! the `slog` crate have been implemented: `ArgminSlogLogger::term` logs to the terminal and //! `ArgminSlogLogger::file` logs to a file in JSON format. Both loggers come with a `*_noblock` //! version which does not block the execution for logging, but may drop log entries when the //! buffer fills up. //! //! ``` //! # extern crate argmin; //! # extern crate ndarray; //! # use argmin::testfunctions::{rosenbrock_2d, rosenbrock_2d_derivative, rosenbrock_2d_hessian}; //! # use argmin::prelude::*; //! # use argmin::solver::gradientdescent::SteepestDescent; //! # use argmin::solver::linesearch::MoreThuenteLineSearch; //! # use serde::{Serialize, Deserialize}; //! # //! # #[derive(Clone, Default, Serialize, Deserialize)] //! # struct Rosenbrock { //! # a: f64, //! # b: f64, //! # } //! # impl ArgminOp for Rosenbrock { //! # type Param = ndarray::Array1<f64>; //! # type Output = f64; //! # type Hessian = (); //! # fn apply(&self, p: &Self::Param) -> Result<Self::Output, Error> { //! # Ok(rosenbrock_2d(&p.to_vec(), self.a, self.b)) //! # } //! # fn gradient(&self, p: &Self::Param) -> Result<Self::Param, Error> { //! # Ok(ndarray::Array1::from_vec(rosenbrock_2d_derivative(&p.to_vec(), self.a, self.b))) //! # } //! # } //! # fn run() -> Result<(), Error> { //! # let cost = Rosenbrock { a: 1.0, b: 100.0 }; //! # let init_param = ndarray::Array1::from_vec(vec![-1.2, 1.0]); //! # let linesearch = MoreThuenteLineSearch::new(cost.clone()); //! let mut solver = SteepestDescent::new(cost, init_param, linesearch)?; //! # solver.set_max_iters(10); //! // Log to the terminal //! solver.add_logger(ArgminSlogLogger::term()); //! // Log to the terminal without blocking //! solver.add_logger(ArgminSlogLogger::term_noblock()); //! // Log to the file `log1.log` //! solver.add_logger(ArgminSlogLogger::file("log1.log")?); //! // Log to the file `log2.log` without blocking //! solver.add_logger(ArgminSlogLogger::file_noblock("log2.log")?); //! # solver.run()?; //! # Ok(()) //! # } //! # fn main() { //! # if let Err(ref e) = run() { //! # println!("{} {}", e.as_fail(), e.backtrace()); //! # std::process::exit(1); //! # } //! # } //! ``` //! //! # Checkpoints //! //! The longer an optimization runs, the higher the probability that something crashes. //! Particularly for optimizations which are running for days, weeks or even longer, this can //! become a problem. To mitigate this problem, it is possible in argmin to save checkpoints. //! Such a checkpoint is a serialization of an `ArgminSolver` object and can be loaded again and //! resumed. //! The `CheckpointMode` defines how often checkpoints are saved and is either `Never` (default), //! `Always` (every iteration) or `Every(u64)` (every Nth iteration). It is set via the setter //! method `set_checkpoint_mode()` which is implemented for every `ArgminSolver`. //! In addition, the directory where the checkpoints and a prefix for every file can be set via //! `set_checkpoint_dir()` and `set_checkpoint_prefix`, respectively. //! //! The following example illustrates the usage. Note that this example is only for illustration //! and does not make much sense. Please scroll down for a more practical example. //! //! ``` //! // [Imports omited] //! //! # extern crate argmin; //! # extern crate ndarray; //! # use argmin::prelude::*; //! # use argmin::solver::linesearch::MoreThuenteLineSearch; //! # use argmin::solver::quasinewton::BFGS; //! # use argmin::testfunctions::rosenbrock; //! # use argmin_core::finitediff::*; //! # use ndarray::{array, Array1, Array2}; //! # use serde::{Deserialize, Serialize}; //! # #[derive(Clone, Default, Serialize, Deserialize)] //! # struct Rosenbrock { //! # a: f64, //! # b: f64, //! # } //! # impl ArgminOp for Rosenbrock { //! # type Param = Array1<f64>; //! # type Output = f64; //! # type Hessian = Array2<f64>; //! # fn apply(&self, p: &Self::Param) -> Result<Self::Output, Error> { //! # Ok(rosenbrock(&p.to_vec(), self.a, self.b)) //! # } //! # fn gradient(&self, p: &Self::Param) -> Result<Self::Param, Error> { //! # Ok((*p).forward_diff(&|x| rosenbrock(&x.to_vec(), self.a, self.b))) //! # } //! # } //! # fn run() -> Result<(), Error> { //! // Define cost function //! let cost = Rosenbrock { a: 1.0, b: 100.0 }; //! let init_param: Array1<f64> = array![-1.2, 1.0, -10.0, 2.0, 3.0, 2.0, 4.0, 10.0]; //! let init_hessian: Array2<f64> = Array2::eye(8); //! let linesearch = MoreThuenteLineSearch::new(cost.clone()); //! //! // Set up solver //! let mut solver = BFGS::new(cost, init_param, init_hessian, linesearch); //! //! // Set maximum number of iterations //! solver.set_max_iters(30); //! //! // Attach a logger //! solver.add_logger(ArgminSlogLogger::term()); //! //! // -------------------------------------------------------------------------------------------- //! // Set up checkpoints //! // -------------------------------------------------------------------------------------------- //! //! // Specify the directory where the checkpoints are saved //! solver.set_checkpoint_dir(".checkpoints"); //! //! // Specifiy the prefix for each file //! solver.set_checkpoint_name("bfgs"); //! //! // Set the `CheckpointMode` which can be `Never` (default), //! // `Always` (every iteration) or `Every(u64)` (every Nth iteration). //! solver.set_checkpoint_mode(CheckpointMode::Every(10)); //! //! // Run solver //! solver.run()?; //! # // Wait a second (lets the logger flush everything before printing again) //! # std::thread::sleep(std::time::Duration::from_secs(1)); //! //! println!("-------------------------------------------"); //! println!("LOADING CHECKPOINT AND RUNNING SOLVER AGAIN"); //! println!("-------------------------------------------"); //! //! // now load the same solver from a checkpoint //! // In order to properly deserialize, the exact type of //! // the solver needs to be specified. //! let mut loaded_solver: BFGS<Rosenbrock, MoreThuenteLineSearch<Rosenbrock>> = //! BFGS::from_checkpoint(".checkpoints/bfgs.arg")?; //! //! // Loggers cannot be serialized, therefore they need to be added again //! loaded_solver.add_logger(ArgminSlogLogger::term()); //! //! // Run solver //! loaded_solver.run()?; //! # // Wait a second (lets the logger flush everything before printing again) //! # std::thread::sleep(std::time::Duration::from_secs(1)); //! //! // Print result //! println!("-------------------------------------------"); //! println!("Initial run"); //! println!("-------------------------------------------"); //! println!("{}", solver.result()); //! //! println!("-------------------------------------------"); //! println!("Run from checkpoint"); //! println!("-------------------------------------------"); //! println!("{}", loaded_solver.result()); //! # Ok(()) //! # } //! # fn main() { //! # if let Err(ref e) = run() { //! # println!("{} {}", e.as_fail(), e.backtrace()); //! # std::process::exit(1); //! # } //! # } //! ``` //! //! A more practical way of using the checkpoints feature is shown in the following example. //! This will either load an existing checkpoint if one exists or it will create a new solver. Type //! inference takes care of the return type of `ArgminSolver::from_checkpoint(...)`. This way, the //! binary can easily be restarted after a crash and will automatically resume from the latest //! checkpoint. //! //! ```rust //! # extern crate argmin; //! # extern crate ndarray; //! # use argmin::prelude::*; //! # use argmin::solver::linesearch::MoreThuenteLineSearch; //! # use argmin::solver::quasinewton::BFGS; //! # use argmin::testfunctions::rosenbrock; //! # use argmin_core::finitediff::*; //! # use ndarray::{array, Array1, Array2}; //! # use serde::{Deserialize, Serialize}; //! # //! # #[derive(Clone, Default, Serialize, Deserialize)] //! # struct Rosenbrock { //! # a: f64, //! # b: f64, //! # } //! # //! # impl ArgminOp for Rosenbrock { //! # type Param = Array1<f64>; //! # type Output = f64; //! # type Hessian = Array2<f64>; //! # //! # fn apply(&self, p: &Self::Param) -> Result<Self::Output, Error> { //! # Ok(rosenbrock(&p.to_vec(), self.a, self.b)) //! # } //! # //! # fn gradient(&self, p: &Self::Param) -> Result<Self::Param, Error> { //! # Ok((*p).forward_diff(&|x| rosenbrock(&x.to_vec(), self.a, self.b))) //! # } //! # } //! # //! # fn run() -> Result<(), Error> { //! # // checkpoint directory //! # //! # // Define cost function //! # let cost = Rosenbrock { a: 1.0, b: 100.0 }; //! # //! # // 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_hessian: Array2<f64> = Array2::eye(8); //! # //! # // set up a line search //! # let linesearch = MoreThuenteLineSearch::new(cost.clone()); //! # //! # // Set up solver //! let mut solver = match BFGS::from_checkpoint(".checkpoints/bfgs.arg") { //! Ok(solver) => solver, //! Err(_) => BFGS::new(cost, init_param, init_hessian, linesearch), //! }; //! # Ok(()) //! # } //! # //! # fn main() { //! # if let Err(ref e) = run() { //! # println!("{} {}", e.as_fail(), e.backtrace()); //! # std::process::exit(1); //! # } //! # } //! //! ``` //! //! # Writers //! //! Writers can be used to handle parameter vectors in some way during the optimization //! (suggestions for a better name are more than welcome!). Usually, this can be used to save the //! intermediate parameter vectors somewhere. Currently, different modes are supported: //! //! * `WriterMode::Never`: Don't do anything. //! * `WriterMode::Always`: Process parameter vector in every iteration. //! * `WriterMode::Every(i)`: Process parameter vector in every i-th iteration. //! * `WriterMode::NewBest`: Process parameter vector whenever there is a new best one. //! //! The following example creates two writers of the type `WriteToFile` which serializes the //! parameter vector using either `serde_json` or `bincode`. The first writer saves the parameters //! in every third iteration (as JSON), while the second one saves only the new best ones (using //! `bincode`). //! Both are attached to a solver using the `add_writer(...)` method of `ArgminSolver` before the //! solver is run. //! //! ```rust //! // [Imports omited] //! # extern crate argmin; //! # extern crate ndarray; //! # use argmin::prelude::*; //! # use argmin::solver::linesearch::MoreThuenteLineSearch; //! # use argmin::solver::quasinewton::BFGS; //! # use argmin::testfunctions::rosenbrock; //! # use argmin_core::finitediff::*; //! # use ndarray::{array, Array1, Array2}; //! # use serde::{Deserialize, Serialize}; //! # use std::sync::Arc; //! # //! # #[derive(Clone, Default, Serialize, Deserialize)] //! # struct Rosenbrock { //! # a: f64, //! # b: f64, //! # } //! # //! # impl ArgminOp for Rosenbrock { //! # type Param = Array1<f64>; //! # type Output = f64; //! # type Hessian = Array2<f64>; //! # //! # fn apply(&self, p: &Self::Param) -> Result<Self::Output, Error> { //! # Ok(rosenbrock(&p.to_vec(), self.a, self.b)) //! # } //! # //! # fn gradient(&self, p: &Self::Param) -> Result<Self::Param, Error> { //! # Ok((*p).forward_diff(&|x| rosenbrock(&x.to_vec(), self.a, self.b))) //! # } //! # } //! # //! # fn run() -> Result<(), Error> { //! # // Define cost function //! # let cost = Rosenbrock { a: 1.0, b: 100.0 }; //! # //! # // 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_hessian: Array2<f64> = Array2::eye(8); //! # //! # // set up a line search //! # let linesearch = MoreThuenteLineSearch::new(cost.clone()); //! # //! # // Set up solver //! # let mut solver = BFGS::new(cost, init_param, init_hessian, linesearch); //! # //! # // Set maximum number of iterations //! # solver.set_max_iters(10); //! # //! # // Attach a logger //! # solver.add_logger(ArgminSlogLogger::term()); //! # //! // Create writer //! let mut writer1 = WriteToFile::new("params", "param"); //! //! // Only save every 3 iterations //! writer1.set_mode(WriterMode::Every(3)); //! //! // Set serializer to JSON //! writer1.set_serializer(WriteToFileSerializer::JSON); //! //! // Create writer which only saves new best ones //! let mut writer2 = WriteToFile::new("params", "best"); //! //! // Only save new best //! writer2.set_mode(WriterMode::NewBest); //! //! // Set serializer to `bincode` //! writer2.set_serializer(WriteToFileSerializer::Bincode); //! //! // Attach writers //! solver.add_writer(Arc::new(writer1)); //! solver.add_writer(Arc::new(writer2)); //! # //! # // Run solver //! # solver.run()?; //! # //! # // Wait a second (lets the logger flush everything before printing again) //! # std::thread::sleep(std::time::Duration::from_secs(1)); //! # //! # // Print result //! # println!("{}", solver.result()); //! # Ok(()) //! # } //! # //! # fn main() { //! # if let Err(ref e) = run() { //! # println!("{} {}", e.as_fail(), e.backtrace()); //! # std::process::exit(1); //! # } //! # } //! //! ``` //! //! # Implementing an optimization algorithm //! //! In this section we are going to implement the Landweber solver, which essentially is a special //! form of gradient descent. In iteration `k`, the new parameter vector `x_{k+1}` is calculated //! from the previous parameter vector `x_k` and the gradient at `x_k` according to the following //! update rule: //! //! `x_{k+1} = x_k - omega * \nabla f(x_k)` //! //! In order to implement this using the argmin framework, one first needs to define a struct which //! holds data/parameters needed during the execution of the algorithm. In addition a field with //! the name `base` and type `ArgminBase<'a, T, U, H>` is needed, where `T` is the type of the //! parameter vector, `U` is the type of the return values of the cost function and `H` is the type //! of the Hessian (which can be `()` if not available). //! //! Deriving `ArgminSolver` for the struct using `#[derive(ArgminSolver)]` implements most of the //! API. What remains to be implemented for the struct is a constructor and `ArgminNextIter`. The //! latter is essentially an implementation of a single iteration of the algorithm. //! //! ``` //! // needed for `#[derive(ArgminSolver)]` //! # extern crate argmin_codegen; //! use argmin_codegen::ArgminSolver; //! use argmin::prelude::*; //! use std::default::Default; //! use serde::{Serialize, Deserialize}; //! //! // The `Landweber` struct holds the `omega` parameter and has a field `base` which is of type //! // `ArgminBase`. The struct is generic over the ArgminOp `O` which holds type information about //! // the parameter vector which (in this particular case) has to implement //! // `ArgminScaledSub<T, f64>`, which is neede for the update rule. //! // Deriving `ArgminSolver` implements a large portion of the API and provides many convenience //! // functions. It requires that `ArgminIter` is implemented on `Landweber` as well. //! #[derive(ArgminSolver, Serialize, Deserialize)] //! pub struct Landweber<O> //! where //! O::Param: ArgminScaledSub<O::Param, f64, O::Param>, //! O: ArgminOp, //! { //! omega: f64, //! base: ArgminBase<O>, //! } //! //! // For convenience, a constructor can/should be implemented //! impl<O> Landweber<O> //! where //! O::Param: ArgminScaledSub<O::Param, f64, O::Param>, //! O: ArgminOp, //! { //! pub fn new( //! cost_function: O, //! omega: f64, //! init_param: O::Param, //! ) -> Result<Self, Error> { //! Ok(Landweber { //! omega, //! base: ArgminBase::new(cost_function, init_param), //! }) //! } //! } //! //! // This implements a single iteration of the optimization algorithm. //! impl<O> ArgminIter for Landweber<O> //! where //! O::Param: ArgminScaledSub<O::Param, f64, O::Param>, //! O: ArgminOp, //! { //! type Param = O::Param; //! type Output = O::Output; //! type Hessian = O::Hessian; //! //! fn next_iter(&mut self) -> Result<ArgminIterData<Self::Param>, Error> { //! // Obtain current parameter vector //! // The method `cur_param()` has been implemented by deriving `ArgminSolver`. //! let param = self.cur_param(); //! // Compute gradient at current parameter vector `param` //! // The method `gradient()` has been implemented by deriving `ArgminSolver`. //! let grad = self.gradient(¶m)?; //! // Calculate new parameter vector based on update rule //! let new_param = param.scaled_sub(&self.omega, &grad); //! // Return new parameter vector. Since there is no need to compute the cost function //! // value, we return 0.0 instead. //! let out = ArgminIterData::new(new_param, 0.0); //! Ok(out) //! } //! } //! # fn main() { //! # } //! ``` #![warn(missing_docs)] #![feature(custom_attribute)] #![feature(unrestricted_attribute_tokens)] #![allow(unused_attributes)] // Explicitly disallow EQ comparison of floats. (This clippy lint is denied by default; however, // this is just to make sure that it will always stay this way.) #![deny(clippy::float_cmp)] extern crate argmin_core; #[macro_use] extern crate argmin_codegen; extern crate argmin_testfunctions; extern crate rand; /// Definition of all relevant traits and types pub mod prelude; /// Solvers pub mod solver; /// Macros #[macro_use] mod macros; // #[cfg(test)] // use macros::*; use argmin_core::*; /// Testfunctions pub mod testfunctions { //! # Testfunctions //! //! Reexport of `argmin-testfunctions`. pub use argmin_testfunctions::*; }