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DOC: optimize: remove inadvertent block quote indentation #13106

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DOC: optimize: remove inadvertent block quote indentation #13106

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cfff7a1
DOC: optimize: remove inadvertent block quote indentation
mdhaber Nov 22, 2020
c57b2cf
DOC: optimize: address @andyfaff comments in gh-13106
mdhaber Nov 22, 2020
99cde83
DOC: optimize: fix indentation in slsqp docstring; fix broyden descr…
mdhaber Dec 10, 2020
89a1c37
DOC: optimize: fix broyden documentation in its other location
mdhaber Dec 21, 2020
1550906
Merge branch 'master' into remove_unnecessary_indent
mdhaber Sep 3, 2021
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10 changes: 5 additions & 5 deletions scipy/optimize/_basinhopping.py
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Expand Up @@ -368,11 +368,11 @@ def basinhopping(func, x0, niter=100, T=1.0, stepsize=0.5,
Extra keyword arguments to be passed to the local minimizer
``scipy.optimize.minimize()`` Some important options could be:

method : str
The minimization method (e.g. ``"L-BFGS-B"``)
args : tuple
Extra arguments passed to the objective function (``func``) and
its derivatives (Jacobian, Hessian).
method : str
The minimization method (e.g. ``"L-BFGS-B"``)
args : tuple
Extra arguments passed to the objective function (``func``) and
its derivatives (Jacobian, Hessian).

take_step : callable ``take_step(x)``, optional
Replace the default step-taking routine with this routine. The default
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10 changes: 6 additions & 4 deletions scipy/optimize/_constraints.py
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Expand Up @@ -47,8 +47,10 @@ class NonlinearConstraint(object):
where element (i, j) is the partial derivative of f[i] with
respect to x[j]). The keywords {'2-point', '3-point',
'cs'} select a finite difference scheme for the numerical estimation.
A callable must have the following signature:
``jac(x) -> {ndarray, sparse matrix}, shape (m, n)``.
A callable must have the following signature::

jac(x) -> {ndarray, sparse matrix}, shape (m, n)

Default is '2-point'.
hess : {callable, '2-point', '3-point', 'cs', HessianUpdateStrategy, None}, optional
Method for computing the Hessian matrix. The keywords
Expand All @@ -57,8 +59,8 @@ class NonlinearConstraint(object):
`HessianUpdateStrategy` interface can be used to approximate the
Hessian. Currently available implementations are:

- `BFGS` (default option)
- `SR1`
- `BFGS` (default option)
- `SR1`

A callable must return the Hessian matrix of ``dot(fun, v)`` and
must have the following signature:
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36 changes: 18 additions & 18 deletions scipy/optimize/_differentialevolution.py
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Expand Up @@ -54,18 +54,18 @@ def differential_evolution(func, bounds, args=(), strategy='best1bin',
strategy : str, optional
The differential evolution strategy to use. Should be one of:

- 'best1bin'
- 'best1exp'
- 'rand1exp'
- 'randtobest1exp'
- 'currenttobest1exp'
- 'best2exp'
- 'rand2exp'
- 'randtobest1bin'
- 'currenttobest1bin'
- 'best2bin'
- 'rand2bin'
- 'rand1bin'
- 'best1bin'
- 'best1exp'
- 'rand1exp'
- 'randtobest1exp'
- 'currenttobest1exp'
- 'best2exp'
- 'rand2exp'
- 'randtobest1bin'
- 'currenttobest1bin'
- 'best2bin'
- 'rand2bin'
- 'rand1bin'

The default is 'best1bin'.
maxiter : int, optional
Expand Down Expand Up @@ -122,12 +122,12 @@ def differential_evolution(func, bounds, args=(), strategy='best1bin',
Specify which type of population initialization is performed. Should be
one of:

- 'latinhypercube'
- 'random'
- array specifying the initial population. The array should have
shape ``(M, len(x))``, where M is the total population size and
len(x) is the number of parameters.
`init` is clipped to `bounds` before use.
- 'latinhypercube'
- 'random'
- array specifying the initial population. The array should have
shape ``(M, len(x))``, where M is the total population size and
len(x) is the number of parameters.
`init` is clipped to `bounds` before use.

The default is 'latinhypercube'. Latin Hypercube sampling tries to
maximize coverage of the available parameter space. 'random'
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6 changes: 3 additions & 3 deletions scipy/optimize/_dual_annealing.py
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Expand Up @@ -496,9 +496,9 @@ def dual_annealing(func, bounds, args=(), maxiter=1000,
latest minimum found, and ``context`` has value in [0, 1, 2], with the
following meaning:

- 0: minimum detected in the annealing process.
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the documentation could do with a little polishing in this section, but that's out of scope of this PR.

- 1: detection occurred in the local search process.
- 2: detection done in the dual annealing process.
- 0: minimum detected in the annealing process.
- 1: detection occurred in the local search process.
- 2: detection done in the dual annealing process.

If the callback implementation returns True, the algorithm will stop.
x0 : ndarray, shape(n,), optional
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40 changes: 24 additions & 16 deletions scipy/optimize/_linprog.py
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Expand Up @@ -65,13 +65,17 @@ def linprog_verbose_callback(res):
feasible solution is sought and the T has an additional row
representing an alternate objective function.
status : int
An integer representing the exit status of the optimization::
An integer representing the exit status of the optimization:

0 : Optimization terminated successfully
1 : Iteration limit reached
2 : Problem appears to be infeasible
3 : Problem appears to be unbounded
4 : Serious numerical difficulties encountered
``0`` : Optimization terminated successfully
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``1`` : Iteration limit reached

``2`` : Problem appears to be infeasible

``3`` : Problem appears to be unbounded

``4`` : Serious numerical difficulties encountered

nit : int
The number of iterations performed.
Expand Down Expand Up @@ -142,13 +146,17 @@ def linprog_terse_callback(res):
feasible solution is sought and the T has an additional row
representing an alternate objective function.
status : int
An integer representing the exit status of the optimization::
An integer representing the exit status of the optimization:
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This is another thing that shows up occasionally - rendering return code lists as literal blocks. One could argue that it's supposed to be that way, but I think it's more important for it to be consistent.

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This change isn't being displayed correctly. Comes out as a single sentence. https://22669-1460385-gh.circle-artifacts.com/0/html-scipyorg/generated/scipy.optimize.linprog_verbose_callback.html#scipy.optimize.linprog_verbose_callback

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Literal blocks are easier to align on the screen, otherwise you get wonky lists which look quite ugly.

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@mdhaber mdhaber Nov 22, 2020 •
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Literal blocks are easier to align on the screen, otherwise you get wonky lists which look quite ugly.

I agree that the bullets are too far to the left, but isn't that more of a problem with the Sphinx theme? I think that's something we can (and should) fix across the board with a change in the right place.

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Thanks. It was supposed to be more like:
image

I'll fix.

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@mdhaber mdhaber Dec 21, 2020 •
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Looks like I did fix this. Now it's:
image

https://23582-1460385-gh.circle-artifacts.com/0/html-scipyorg/generated/scipy.optimize.linprog_verbose_callback.html#scipy.optimize.linprog_verbose_callback


``0`` : Optimization terminated successfully

0 : Optimization terminated successfully
1 : Iteration limit reached
2 : Problem appears to be infeasible
3 : Problem appears to be unbounded
4 : Serious numerical difficulties encountered
``1`` : Iteration limit reached

``2`` : Problem appears to be infeasible

``3`` : Problem appears to be unbounded

``4`` : Serious numerical difficulties encountered

nit : int
The number of iterations performed.
Expand Down Expand Up @@ -263,10 +271,10 @@ def linprog(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None,

``4`` : Numerical difficulties encountered.

nit : int
The current iteration number.
message : str
A string descriptor of the algorithm status.
nit : int
The current iteration number.
message : str
A string descriptor of the algorithm status.

Callback functions are not currently supported by the HiGHS methods.

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