JijModeling 2.8.0 Release Notes

Feature Enhancements

“Set” is now called “Stream”

Previously, JijModeling called the type representing “a sequence of values that can be iterated over” Set. However, because a mathematical “set” is a collection with neither duplicates nor order, this terminology could be misleading.

Starting with this release, what was previously called Set is now called Stream, and jm.stream has been introduced in place of the deprecated jm.set function. This follows the convention in general-purpose programming languages of calling such “an ordered sequence of values that may contain duplicates” a stream.

import jijmodeling as jm


problem = jm.Problem("stream example")
N = problem.Natural("N")
problem.infer(jm.stream(N))

\[\mathop{\mathrm{Stream}}\left[\mathbb{N}\right]\]

@problem.update
def _(problem: jm.DecoratedProblem, N: jm.Placeholder):
    # Comprehensions are also supported in the Decorator API.
    print(problem.infer(jm.stream(2 * i for i in N if i % 2 == 0)))

Stream[natural]

jm.set remains available as an alias of jm.stream, but because it is scheduled for removal, we recommend migrating to jm.stream as soon as possible.

Improvement to gendict’s LaTeX output

The \(\LaTeX\) output for gendict expressions is now styled closer to genarrays.

import jijmodeling as jm


problem = jm.Problem("gendict example")
K = problem.CategoryLabel("K")
a = problem.Float("a", dict_keys=K)
x = problem.BinaryVar("x", dict_keys=K)
Sums = problem.NamedExpr("Sums", jm.gendict(lambda k: a[k] * x[k], K))


problem

\[\begin{array}{rl} \text{Problem}\colon &\text{gendict example}\\\displaystyle \min &\displaystyle 0\\&\\\text{where}&\\&\text{Decision Variables:}\\&\qquad \begin{alignedat}{2}{x}_{i}&\in \left\{0,1\right\}&\qquad &\text{a dictionary of }\text{binary}\text{ decision variables}\\&\forall i\in K&&\\\end{alignedat}\\&\\&\text{Placeholders:}\\&\qquad \begin{alignedat}{2}{a}_{i}&\in \mathbb{R}&\qquad &\text{a dictionary of placeholders in }\mathbb{R}\\&\forall i\in \mathrm{K}&&\\\end{alignedat}\\&\\&\text{Category Labels:}\\&\qquad \begin{array}{rl} K&\text{Category Label}\end{array} \\&\\&\text{Named Expressions:}\\&\qquad \begin{alignedat}{2}Sums&={\left\{ {a}_{k}\cdot {x}_{k}\right\} }_{k\in K}&\quad &\in \mathop{\mathrm{TotalDict}}\left[K;\mathbb{R}\right]\\\end{alignedat}\end{array} \]

if clauses in gendict comprehensions

The Decorator API now supports an if clause after a single for clause in a gendict comprehension. This makes it possible to flexibly define dictionaries with restricted domains using gendict comprehensions.

import jijmodeling as jm


@jm.Problem.define("gendict-if")
def problem(problem: jm.DecoratedProblem):
    N = problem.Length()
    c = problem.Float(dict_keys=N)
    A = problem.NamedExpr(jm.gendict(c[i] * 2 for i in N if i != 0))


problem

\[\begin{array}{rl} \text{Problem}\colon &\text{gendict-if}\\\displaystyle \min &\displaystyle 0\\&\\\text{where}&\\&\\&\text{Placeholders:}\\&\qquad \begin{alignedat}{2}{c}_{i}&\in \mathbb{R}&\qquad &\text{a dictionary of placeholders in }\mathbb{R}\\&\forall i\in \left\{0,\ldots ,N-1\right\}&&\\&&&\\N&\in \mathbb{N}&\qquad &\text{a scalar placeholder in }\mathbb{N}\\\end{alignedat}\\&\\&\text{Named Expressions:}\\&\qquad \begin{alignedat}{2}A&={\left\{ {c}_{i}\cdot 2\right\} }_{\begin{subarray}{l} i\in \left\{0,\ldots ,N-1\right\}\\i\neq 0\end{subarray} }&\quad &\in \mathop{\mathrm{TotalDict}}\left[\left\{i\mid i\in \left\{0,\ldots ,N-1\right\},i\neq 0\right\};\mathbb{R}\right]\\\end{alignedat}\end{array} \]

The following example uses multiple if clauses.

import jijmodeling as jm


@jm.Problem.define("gendict-tuple-if")
def problem(problem: jm.DecoratedProblem):
    N = problem.Length()
    L = problem.CategoryLabel()
    avoid = problem.Placeholder(dtype=L)
    c = problem.Float(dict_keys=(N, L))
    OffDiag = problem.NamedExpr(
        jm.gendict(i + c[i, l] for (i, l) in (N, L) if i % 2 != 0 if l != avoid)
    )


problem

\[\begin{array}{rl} \text{Problem}\colon &\text{gendict-tuple-if}\\\displaystyle \min &\displaystyle 0\\&\\\text{where}&\\&\\&\text{Placeholders:}\\&\qquad \begin{alignedat}{2}avoid&\in \mathrm{L}&\qquad &\text{a scalar placeholder in }\mathrm{L}\\&&&\\{c}_{i,j}&\in \mathbb{R}&\qquad &\text{a dictionary of placeholders in }\mathbb{R}\\&\forall i\in \left\{0,\ldots ,N-1\right\},\;\forall j\in \mathrm{L}&&\\&&&\\N&\in \mathbb{N}&\qquad &\text{a scalar placeholder in }\mathbb{N}\\\end{alignedat}\\&\\&\text{Category Labels:}\\&\qquad \begin{array}{rl} L&\text{Category Label}\end{array} \\&\\&\text{Named Expressions:}\\&\qquad \begin{alignedat}{2}OffDiag&={\left\{ i+{c}_{i,l}\right\} }_{\begin{subarray}{l} i\in \left\{0,\ldots ,N-1\right\}\\l\in L\\i\bmod 2\neq 0\\l\neq avoid\end{subarray} }&\quad &\in \mathop{\mathrm{TotalDict}}\left[\left\{\left\langle i,l\right\rangle \mid i\in \left\{0,\ldots ,N-1\right\},l\in L,i\bmod 2\neq 0,l\neq avoid\right\};\mathbb{R}\right]\\\end{alignedat}\end{array} \]

Bugfixes

Fixed excessive memory consumption during constraint detection with unbounded decision variables

Compiling a model in which a decision variable was made unbounded by giving it an infinite bound — for example, upper_bound=float("inf") — could consume memory without limit when constraint detection was enabled.

This bug has been fixed in this release, and such models now compile successfully even when constraint detection is enabled. In addition, an error is now raised at definition time when a bound is NaN or when the bounds are inherently infeasible, such as when the upper bound is negative infinity.

import jijmodeling as jm

@jm.Problem.define("production", sense=jm.ProblemSense.MINIMIZE)
def problem(problem: jm.DecoratedProblem):
    T = problem.Length()
    demand = problem.Float(shape=(T,))
    x = problem.ContinuousVar(
        lower_bound=0.0, upper_bound=float("inf"), shape=(T,)
    )

    problem += jm.sum(x[t] for t in T)
    problem += problem.Constraint("constr", [x[t] >= demand[t] for t in T])


problem.eval({"T": 3, "demand": [1.0, 2.0, 3.0]})

Instance(raw=<builtins.Instance object at 0xbfee58a00>, annotations={})

Fixed an issue where dictionaries could not be mapped

map() on a dictionary should return a dictionary whose values have been transformed by the given function, but previously it raised an exception during type checking. Starting with this release, it returns a dictionary with the same set of keys as the original and with the function applied to corresponding values.

import jijmodeling as jm

problem = jm.Problem("Mapped Dicts")
N = problem.Natural("N")
L = problem.CategoryLabel("L")
x = problem.PartialDict(
    "x",
    dict_keys=(L, L),
    dtype=(L, N),
)
problem.infer(x)

\[\mathop{\mathrm{PartialDict}}\left[\mathrm{L}\times \mathrm{L};\mathrm{L}\times N\right]\]

problem.infer(x.map(lambda l, n: n))

\[\mathop{\mathrm{PartialDict}}\left[\mathrm{L}\times \mathrm{L};N\right]\]

The example above uses a PartialDict, but a TotalDict produces the similar result.