One construct per model#

For each built-in operator, the smallest model that declares it, beside the equation it prints. The reference page shows the same equations as one table. This page shows the file that produced each one.

Each is a whole model rather than a fragment, so a model whose operator changed shape stops loading in CI, in the run that would otherwise have shipped the old math.

`sum(array)`#

examples/operators/sum_all.yaml

description: Every dimension at once — `sum(array)` names none of them and takes them all.

dimensions:
  snapshot: { dtype: int }
  generator: { dtype: str }

parameters:
  budget: { dims: [] }

variables:
  p:
    dims: [snapshot, generator]
    bounds: { lower: 0 }

constraints:
  fleet_budget:
    dims: []
    expression: sum(p) <= budget

objective: { sense: minimize, expression: sum(p) }

\(\sum_{t \in \mathcal{T},\ g \in \mathcal{G}} p_{t,g} \le \mathrm{budget}\)

`sum(array, over=dim)`#

examples/operators/sum.yaml

description: The plain reduction — `sum(array, over=dim)` collapses one dimension.

dimensions:
  snapshot: { dtype: int }
  generator: { dtype: str }

parameters:
  limit: { dims: [snapshot] }

variables:
  p:
    dims: [snapshot, generator]
    bounds: { lower: 0 }

constraints:
  fleet_total:
    dims: [snapshot]
    expression: sum(p, over=generator) <= limit

objective: { sense: minimize, expression: sum(p) }

\(\sum_{g \in \mathcal{G}} p_{t,g} \le \mathrm{limit}_{t} \qquad \forall\, t \in \mathcal{T}\)

`sum(array, by=relation)`#

examples/operators/sum_by.yaml

description: >-
  The membership reduction — `sum(array, by=relation)` lands the result on the
  column the relation is walked to, which is what makes topology data rather than
  structure.

dimensions:
  snapshot: { dtype: int }
  generator: { dtype: str }
  bus: { dtype: str }

relations:
  gen_bus: { columns: [generator, bus], key: generator }

parameters:
  limit: { dims: [snapshot, bus] }

variables:
  p:
    dims: [snapshot, generator]
    bounds: { lower: 0 }

constraints:
  bus_total:
    dims: [snapshot, bus]
    expression: sum(p, by=gen_bus) <= limit

objective: { sense: minimize, expression: sum(p) }

\(\sum_{g \in \mathcal{G} \,:\, \mathrm{gen\_bus}(g) = b} p_{t,g} \le \mathrm{limit}_{t,b} \qquad \forall\, t \in \mathcal{T},\ b \in \mathcal{B}\)

`sum(array, by=[relation, …])`#

examples/operators/sum_by_relations.yaml

description: >-
  Grouping through several maps at once — `sum(array, by=[relation, …])` lands
  the result on every dimension the relations map into, which is one grouping
  rather than a composition of two: the generator dimension is consumed once.

dimensions:
  snapshot: { dtype: int }
  generator: { dtype: str }
  bus: { dtype: str }
  technology: { dtype: str }

relations:
  gen_bus: { columns: [generator, bus], key: generator }
  gen_tech: { columns: [generator, technology], key: generator }

parameters:
  limit: { dims: [snapshot, bus, technology] }

variables:
  p:
    dims: [snapshot, generator]
    bounds: { lower: 0 }

constraints:
  bus_technology_total:
    dims: [snapshot, bus, technology]
    expression: sum(p, by=[gen_bus, gen_tech]) <= limit

objective: { sense: minimize, expression: sum(p) }

\(\sum_{g \in \mathcal{G} \,:\, \mathrm{gen\_bus}(g) = b \wedge \mathrm{gen\_tech}(g) = e} p_{t,g} \le \mathrm{limit}_{t,b,e} \qquad \forall\, t \in \mathcal{T},\ b \in \mathcal{B},\ e \in \mathcal{E}\)

`at(array, by=relation)`#

examples/operators/at.yaml

description: >-
  The adjoint of the membership reduction — `at(array, by=relation)` reads one
  coarse value once per fine label pointing at it.

dimensions:
  snapshot: { dtype: int }
  period: { dtype: int }

relations:
  period_of: { columns: [snapshot, period], key: snapshot }

parameters:
  cap: { dims: [period] }

variables:
  p:
    dims: [snapshot]
    bounds: { lower: 0 }

constraints:
  within_cap:
    dims: [snapshot]
    expression: p <= at(cap, by=period_of)

objective: { sense: minimize, expression: sum(p) }

\(p_{t} \le \mathrm{cap}_{\mathrm{period\_of}(t)} \qquad \forall\, t \in \mathcal{T}\)

`shift(array, along=dim, offset=n)`#

examples/operators/shift.yaml

description: >-
  Translation with no edge policy — the vacated position is absent, so the row
  it would have fed is not built.

dimensions:
  snapshot: { dtype: int }

variables:
  p:
    dims: [snapshot]
    bounds: { lower: 0 }

constraints:
  no_faster_than_before:
    dims: [snapshot]
    expression: p <= shift(p, along=snapshot, offset=1)

objective: { sense: minimize, expression: sum(p) }

\(p_{t} \le p_{t - 1} \qquad \forall\, t \in \mathcal{T}\)

`shift(array, along=dim, offset=n, edge='wrap')`#

examples/operators/shift_wrap.yaml

description: >-
  Cyclic translation — the horizon closed on itself, so the first position
  reads the last and nothing is vacated.

dimensions:
  snapshot: { dtype: int }

variables:
  p:
    dims: [snapshot]
    bounds: { lower: 0 }

constraints:
  no_faster_than_before:
    dims: [snapshot]
    expression: p <= shift(p, along=snapshot, offset=1, edge='wrap')

objective: { sense: minimize, expression: sum(p) }

\(p_{t} \le p_{t \ominus 1} \qquad \forall\, t \in \mathcal{T}\)

`shift(array, along=dim, offset=n, edge=v)`#

examples/operators/shift_edge.yaml

description: >-
  Translation with a value at the edge — the vacated position contributes the
  number instead of being absent, so the row survives.

dimensions:
  snapshot: { dtype: int }

variables:
  p:
    dims: [snapshot]
    bounds: { lower: 0 }

constraints:
  no_faster_than_before:
    dims: [snapshot]
    expression: p <= shift(p, along=snapshot, offset=1, edge=0)

objective: { sense: minimize, expression: sum(p) }

\(p_{t} \le p_{t \boxminus_{0} 1} \qquad \forall\, t \in \mathcal{T}\)

`shift(array, along=dim, offset=p, edge=…)`#

examples/operators/shift_by_parameter.yaml

description: >-
  Translation by an offset that differs per entity — `by:` names an integer
  parameter, so each technology is reached by its own lead time rather than by
  one the file had to fix.

dimensions:
  technology: { dtype: str }
  month: { dtype: int }

parameters:
  lead: { dims: [technology], dtype: int }
  demand: { dims: [technology, month] }

variables:
  order:
    dims: [technology, month]
    bounds: { lower: 0 }

constraints:
  arrives_after_its_lead:
    dims: [technology, month]
    expression: shift(order, along=month, offset=lead, edge=0) >= demand

objective: { sense: minimize, expression: sum(order) }

\(\mathit{order}_{t,m \boxminus_{0} \mathrm{lead}} \ge \mathrm{demand}_{t,m} \qquad \forall\, t \in \mathcal{T},\ m \in \mathcal{M}\)

`shift(array, along=dim, offset=n, by=relation)`#

examples/operators/shift_partitioned.yaml

description: >-
  Translation inside a group — each season closed on itself, so a season's first
  snapshot reads that season's last and no level crosses the boundary.

dimensions:
  snapshot: { dtype: int }
  season: { dtype: str }

relations:
  season_of: { columns: [snapshot, season], key: snapshot }

variables:
  p:
    dims: [snapshot]
    bounds: { lower: 0 }

constraints:
  no_faster_than_before_in_season:
    dims: [snapshot]
    expression: p <= shift(p, along=snapshot, offset=1, edge='wrap', by=season_of)

objective: { sense: minimize, expression: sum(p) }

\(p_{t} \le p_{t \ominus^{\mathrm{season\_of}(t)} 1} \qquad \forall\, t \in \mathcal{T}\)

`sum_back(array, along=dim, window=n)`#

examples/operators/sum_back.yaml

description: >-
  A trailing window of a fixed width: a unit that started in the last three
  hours is still on.

dimensions:
  unit: { dtype: str }
  hour: { dtype: int }

parameters:
  min_up: { dims: [unit], dtype: int }

variables:
  started:
    dims: [unit, hour]
    domain: binary
  on:
    dims: [unit, hour]
    domain: binary

constraints:
  stays_up_its_own_time:
    dims: [unit, hour]
    expression: sum_back(started, along=hour, window=3) <= on

objective: { sense: minimize, expression: sum(on) }

\(\sum_{h' \in \mathcal{H} \,:\, 0 \le h - h' < 3} \mathit{started}_{u,h'} \le \mathit{on}_{u,h} \qquad \forall\, u \in \mathcal{U},\ h \in \mathcal{H}\)

`sum_back(array, along=dim, window=p)`#

examples/operators/sum_back_by_parameter.yaml

description: >-
  A trailing window whose width is data — `within:` names an integer parameter,
  so a unit stays up for its *own* minimum time rather than one the file fixed.

dimensions:
  unit: { dtype: str }
  hour: { dtype: int }

parameters:
  min_up: { dims: [unit], dtype: int }

variables:
  started:
    dims: [unit, hour]
    domain: binary
  on:
    dims: [unit, hour]
    domain: binary

constraints:
  stays_up_its_own_time:
    dims: [unit, hour]
    expression: sum_back(started, along=hour, window=min_up) <= on

objective: { sense: minimize, expression: sum(on) }

\(\sum_{h' \in \mathcal{H} \,:\, 0 \le h - h' < \mathrm{min\_up}} \mathit{started}_{u,h'} \le \mathit{on}_{u,h} \qquad \forall\, u \in \mathcal{U},\ h \in \mathcal{H}\)

`sum_back(array, along=dim, window=p, edge='wrap')`#

examples/operators/sum_back_wrap.yaml

description: >-
  A trailing window on a representative period that repeats, so the window at
  the first hour reaches back into the last.

dimensions:
  unit: { dtype: str }
  hour: { dtype: int }

parameters:
  min_up: { dims: [unit], dtype: int }

variables:
  started:
    dims: [unit, hour]
    domain: binary
  on:
    dims: [unit, hour]
    domain: binary

constraints:
  stays_up_its_own_time:
    dims: [unit, hour]
    expression: sum_back(started, along=hour, window=min_up, edge='wrap') <= on

objective: { sense: minimize, expression: sum(on) }

\(\sum_{h' \in \mathcal{H} \,:\, 0 \le h \ominus h' < \mathrm{min\_up}} \mathit{started}_{u,h'} \le \mathit{on}_{u,h} \qquad \forall\, u \in \mathcal{U},\ h \in \mathcal{H}\)

`sum_back(array, along=dim, window=n, by=relation)`#

examples/operators/sum_back_partitioned.yaml

description: >-
  A window that stops at each group's edge: representative days are separate
  samples rather than consecutive hours, so a window must not reach across the
  boundary between two of them.

dimensions:
  unit: { dtype: str }
  hour: { dtype: int }
  day: { dtype: str }

relations:
  day_of: { columns: [hour, day], key: hour }

variables:
  started:
    dims: [unit, hour]
    domain: binary
  on:
    dims: [unit, hour]
    domain: binary

constraints:
  stays_up_inside_its_day:
    dims: [unit, hour]
    expression: sum_back(started, along=hour, window=3, by=day_of) <= on

objective: { sense: minimize, expression: sum(on) }

\(\sum_{h' \in \mathcal{H} \,:\, 0 \le h -^{\mathrm{day\_of}(h)} h' < 3} \mathit{started}_{u,h'} \le \mathit{on}_{u,h} \qquad \forall\, u \in \mathcal{U},\ h \in \mathcal{H}\)

`dual(constraint)`#

examples/operators/dual.yaml

description: The row dual — `dual(constraint)` reads a solved constraint's shadow price over its own frame.

dimensions:
  snapshot: { dtype: int }

parameters:
  load: { dims: [snapshot] }

variables:
  p:
    dims: [snapshot]
    bounds: { lower: 0 }

constraints:
  balance:
    dims: [snapshot]
    expression: p >= load

expressions:
  price: dual(balance)

objective: { sense: minimize, expression: sum(p) }

\(\mathit{price}_{t} = \lambda_{\mathrm{balance},t} \qquad \forall\, t \in \mathcal{T}\)

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One construct per model#

sum(array)#

sum(array, over=dim)#

sum(array, by=relation)#

sum(array, by=[relation, …])#

at(array, by=relation)#

shift(array, along=dim, offset=n)#

shift(array, along=dim, offset=n, edge='wrap')#

shift(array, along=dim, offset=n, edge=v)#

shift(array, along=dim, offset=p, edge=…)#

shift(array, along=dim, offset=n, by=relation)#

sum_back(array, along=dim, window=n)#

sum_back(array, along=dim, window=p)#

sum_back(array, along=dim, window=p, edge='wrap')#

sum_back(array, along=dim, window=n, by=relation)#

dual(constraint)#