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430 lines
12 KiB
Python
430 lines
12 KiB
Python
"""
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=====================================================
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Optimization and root finding (:mod:`scipy.optimize`)
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=====================================================
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.. currentmodule:: scipy.optimize
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SciPy ``optimize`` provides functions for minimizing (or maximizing)
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objective functions, possibly subject to constraints. It includes
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solvers for nonlinear problems (with support for both local and global
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optimization algorithms), linear programing, constrained
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and nonlinear least-squares, root finding, and curve fitting.
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Common functions and objects, shared across different solvers, are:
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.. autosummary::
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:toctree: generated/
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show_options - Show specific options optimization solvers.
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OptimizeResult - The optimization result returned by some optimizers.
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OptimizeWarning - The optimization encountered problems.
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Optimization
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============
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Scalar functions optimization
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-----------------------------
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.. autosummary::
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:toctree: generated/
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minimize_scalar - Interface for minimizers of univariate functions
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The `minimize_scalar` function supports the following methods:
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.. toctree::
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optimize.minimize_scalar-brent
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optimize.minimize_scalar-bounded
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optimize.minimize_scalar-golden
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Local (multivariate) optimization
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---------------------------------
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.. autosummary::
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:toctree: generated/
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minimize - Interface for minimizers of multivariate functions.
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The `minimize` function supports the following methods:
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.. toctree::
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optimize.minimize-neldermead
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optimize.minimize-powell
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optimize.minimize-cg
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optimize.minimize-bfgs
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optimize.minimize-newtoncg
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optimize.minimize-lbfgsb
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optimize.minimize-tnc
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optimize.minimize-cobyla
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optimize.minimize-slsqp
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optimize.minimize-trustconstr
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optimize.minimize-dogleg
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optimize.minimize-trustncg
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optimize.minimize-trustkrylov
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optimize.minimize-trustexact
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Constraints are passed to `minimize` function as a single object or
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as a list of objects from the following classes:
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.. autosummary::
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:toctree: generated/
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NonlinearConstraint - Class defining general nonlinear constraints.
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LinearConstraint - Class defining general linear constraints.
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Simple bound constraints are handled separately and there is a special class
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for them:
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.. autosummary::
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:toctree: generated/
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Bounds - Bound constraints.
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Quasi-Newton strategies implementing `HessianUpdateStrategy`
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interface can be used to approximate the Hessian in `minimize`
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function (available only for the 'trust-constr' method). Available
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quasi-Newton methods implementing this interface are:
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.. autosummary::
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:toctree: generated/
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BFGS - Broyden-Fletcher-Goldfarb-Shanno (BFGS) Hessian update strategy.
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SR1 - Symmetric-rank-1 Hessian update strategy.
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Global optimization
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-------------------
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.. autosummary::
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:toctree: generated/
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basinhopping - Basinhopping stochastic optimizer.
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brute - Brute force searching optimizer.
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differential_evolution - stochastic minimization using differential evolution.
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shgo - simplicial homology global optimisation
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dual_annealing - Dual annealing stochastic optimizer.
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Least-squares and curve fitting
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===============================
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Nonlinear least-squares
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-----------------------
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.. autosummary::
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:toctree: generated/
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least_squares - Solve a nonlinear least-squares problem with bounds on the variables.
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Linear least-squares
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--------------------
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.. autosummary::
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:toctree: generated/
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nnls - Linear least-squares problem with non-negativity constraint.
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lsq_linear - Linear least-squares problem with bound constraints.
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Curve fitting
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-------------
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.. autosummary::
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:toctree: generated/
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curve_fit -- Fit curve to a set of points.
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Root finding
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============
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Scalar functions
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----------------
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.. autosummary::
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:toctree: generated/
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root_scalar - Unified interface for nonlinear solvers of scalar functions.
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brentq - quadratic interpolation Brent method.
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brenth - Brent method, modified by Harris with hyperbolic extrapolation.
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ridder - Ridder's method.
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bisect - Bisection method.
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newton - Newton's method (also Secant and Halley's methods).
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toms748 - Alefeld, Potra & Shi Algorithm 748.
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RootResults - The root finding result returned by some root finders.
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The `root_scalar` function supports the following methods:
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.. toctree::
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optimize.root_scalar-brentq
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optimize.root_scalar-brenth
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optimize.root_scalar-bisect
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optimize.root_scalar-ridder
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optimize.root_scalar-newton
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optimize.root_scalar-toms748
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optimize.root_scalar-secant
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optimize.root_scalar-halley
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The table below lists situations and appropriate methods, along with
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*asymptotic* convergence rates per iteration (and per function evaluation)
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for successful convergence to a simple root(*).
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Bisection is the slowest of them all, adding one bit of accuracy for each
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function evaluation, but is guaranteed to converge.
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The other bracketing methods all (eventually) increase the number of accurate
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bits by about 50% for every function evaluation.
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The derivative-based methods, all built on `newton`, can converge quite quickly
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if the initial value is close to the root. They can also be applied to
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functions defined on (a subset of) the complex plane.
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+-------------+----------+----------+-----------+-------------+-------------+----------------+
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| Domain of f | Bracket? | Derivatives? | Solvers | Convergence |
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+ + +----------+-----------+ +-------------+----------------+
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| | | `fprime` | `fprime2` | | Guaranteed? | Rate(s)(*) |
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+=============+==========+==========+===========+=============+=============+================+
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| `R` | Yes | N/A | N/A | - bisection | - Yes | - 1 "Linear" |
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| | | | | - brentq | - Yes | - >=1, <= 1.62 |
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| | | | | - brenth | - Yes | - >=1, <= 1.62 |
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| | | | | - ridder | - Yes | - 2.0 (1.41) |
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| | | | | - toms748 | - Yes | - 2.7 (1.65) |
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+-------------+----------+----------+-----------+-------------+-------------+----------------+
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| `R` or `C` | No | No | No | secant | No | 1.62 (1.62) |
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+-------------+----------+----------+-----------+-------------+-------------+----------------+
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| `R` or `C` | No | Yes | No | newton | No | 2.00 (1.41) |
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+-------------+----------+----------+-----------+-------------+-------------+----------------+
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| `R` or `C` | No | Yes | Yes | halley | No | 3.00 (1.44) |
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+-------------+----------+----------+-----------+-------------+-------------+----------------+
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.. seealso::
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`scipy.optimize.cython_optimize` -- Typed Cython versions of zeros functions
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Fixed point finding:
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.. autosummary::
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:toctree: generated/
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fixed_point - Single-variable fixed-point solver.
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Multidimensional
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----------------
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.. autosummary::
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:toctree: generated/
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root - Unified interface for nonlinear solvers of multivariate functions.
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The `root` function supports the following methods:
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.. toctree::
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optimize.root-hybr
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optimize.root-lm
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optimize.root-broyden1
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optimize.root-broyden2
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optimize.root-anderson
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optimize.root-linearmixing
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optimize.root-diagbroyden
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optimize.root-excitingmixing
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optimize.root-krylov
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optimize.root-dfsane
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Linear programming
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==================
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.. autosummary::
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:toctree: generated/
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linprog -- Unified interface for minimizers of linear programming problems.
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The `linprog` function supports the following methods:
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.. toctree::
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optimize.linprog-simplex
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optimize.linprog-interior-point
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optimize.linprog-revised_simplex
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optimize.linprog-highs-ipm
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optimize.linprog-highs-ds
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optimize.linprog-highs
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The simplex, interior-point, and revised simplex methods support callback
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functions, such as:
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.. autosummary::
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:toctree: generated/
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linprog_verbose_callback -- Sample callback function for linprog (simplex).
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Assignment problems
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===================
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.. autosummary::
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:toctree: generated/
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linear_sum_assignment -- Solves the linear-sum assignment problem.
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quadratic_assignment -- Solves the quadratic assignment problem.
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The `quadratic_assignment` function supports the following methods:
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.. toctree::
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optimize.qap-faq
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optimize.qap-2opt
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Utilities
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=========
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Finite-difference approximation
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-------------------------------
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.. autosummary::
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:toctree: generated/
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approx_fprime - Approximate the gradient of a scalar function.
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check_grad - Check the supplied derivative using finite differences.
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Line search
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-----------
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.. autosummary::
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:toctree: generated/
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bracket - Bracket a minimum, given two starting points.
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line_search - Return a step that satisfies the strong Wolfe conditions.
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Hessian approximation
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---------------------
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.. autosummary::
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:toctree: generated/
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LbfgsInvHessProduct - Linear operator for L-BFGS approximate inverse Hessian.
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HessianUpdateStrategy - Interface for implementing Hessian update strategies
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Benchmark problems
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------------------
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.. autosummary::
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:toctree: generated/
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rosen - The Rosenbrock function.
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rosen_der - The derivative of the Rosenbrock function.
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rosen_hess - The Hessian matrix of the Rosenbrock function.
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rosen_hess_prod - Product of the Rosenbrock Hessian with a vector.
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Legacy functions
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================
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The functions below are not recommended for use in new scripts;
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all of these methods are accessible via a newer, more consistent
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interfaces, provided by the interfaces above.
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Optimization
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------------
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General-purpose multivariate methods:
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.. autosummary::
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:toctree: generated/
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fmin - Nelder-Mead Simplex algorithm.
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fmin_powell - Powell's (modified) level set method.
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fmin_cg - Non-linear (Polak-Ribiere) conjugate gradient algorithm.
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fmin_bfgs - Quasi-Newton method (Broydon-Fletcher-Goldfarb-Shanno).
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fmin_ncg - Line-search Newton Conjugate Gradient.
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Constrained multivariate methods:
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.. autosummary::
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:toctree: generated/
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fmin_l_bfgs_b - Zhu, Byrd, and Nocedal's constrained optimizer.
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fmin_tnc - Truncated Newton code.
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fmin_cobyla - Constrained optimization by linear approximation.
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fmin_slsqp - Minimization using sequential least-squares programming.
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Univariate (scalar) minimization methods:
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.. autosummary::
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:toctree: generated/
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fminbound - Bounded minimization of a scalar function.
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brent - 1-D function minimization using Brent method.
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golden - 1-D function minimization using Golden Section method.
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Least-squares
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-------------
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.. autosummary::
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:toctree: generated/
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leastsq - Minimize the sum of squares of M equations in N unknowns.
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Root finding
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------------
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General nonlinear solvers:
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.. autosummary::
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:toctree: generated/
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fsolve - Non-linear multivariable equation solver.
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broyden1 - Broyden's first method.
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broyden2 - Broyden's second method.
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Large-scale nonlinear solvers:
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.. autosummary::
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:toctree: generated/
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newton_krylov
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anderson
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Simple iteration solvers:
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.. autosummary::
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:toctree: generated/
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excitingmixing
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linearmixing
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diagbroyden
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:mod:`Additional information on the nonlinear solvers <scipy.optimize.nonlin>`
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"""
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from .optimize import *
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from ._minimize import *
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from ._root import *
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from ._root_scalar import *
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from .minpack import *
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from .zeros import *
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from .lbfgsb import fmin_l_bfgs_b, LbfgsInvHessProduct
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from .tnc import fmin_tnc
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from .cobyla import fmin_cobyla
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from .nonlin import *
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from .slsqp import fmin_slsqp
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from ._nnls import nnls
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from ._basinhopping import basinhopping
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from ._linprog import linprog, linprog_verbose_callback
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from ._lsap import linear_sum_assignment
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from ._differentialevolution import differential_evolution
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from ._lsq import least_squares, lsq_linear
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from ._constraints import (NonlinearConstraint,
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LinearConstraint,
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Bounds)
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from ._hessian_update_strategy import HessianUpdateStrategy, BFGS, SR1
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from ._shgo import shgo
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from ._dual_annealing import dual_annealing
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from ._qap import quadratic_assignment
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__all__ = [s for s in dir() if not s.startswith('_')]
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from scipy._lib._testutils import PytestTester
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test = PytestTester(__name__)
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del PytestTester
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