PhyslibAlpha.AlgebraicFramework.OrderUnit.Weight.Basic
Weights
i. Overview
A weight is a statistical weight: a number in `[0, ∞]` on each outcome saying how much of it there is, with no requirement that the total be finite or normalized to 1 — hence the `∞` and the fact that weights are compared, never subtracted. They live on `PosCone E`, the space of possible outcomes from `OrderUnit/Basic.lean`.
Being an honest linear map, `Weight E` is automatically an `ℝ≥0`-module in its own right: combining two weights with nonnegative coefficients is again a weight, for free, with no boundedness proof to give (contrast `Effect`, a bounded slice of `E` that needs `Effect.convex` to stay closed under mixing). `Weight.IsState.mix` is the one thing that *does* need proving: that this combination preserves normalization when the coefficients sum to `1`.
ii. Key definitions and results
- `Weight E` - `Weight.IsFaithful`, `Weight.IsFinite`, `Weight.IsSemifinite`, `Weight.IsNormal` : the standard refinements. - `Weight.IsState` : a finite weight normalized at the order unit — an actual state. - `Weight.mix`, `Weight.IsState.mix` : mixing two (state) weights.
iii. Table of contents
- A. Weights on the positive cone
- B. Standard properties of weights
- C. Mixtures
- D. State weights
A. Weights on the positive cone
B. Standard properties of weights
C. Mixtures
D. State weights
17 declarations
Weight on as an -linear map to
Let be an ordered real vector space. A weight on is defined as an -linear map from the positive cone to the set of extended non-negative real numbers . It assigns a value to each outcome in the positive cone such that for all and .
Extensionality of weights: if for all
Let be an ordered real vector space. For any two weights and on , if for every element in the positive cone , then .
for a weight
Let be an ordered real vector space and let be a weight on , which is an -linear map from the positive cone to the extended non-negative real numbers . Then , where denotes the zero element of .
Additivity of Weights:
Let be an ordered real vector space and be its positive cone. For any weight on and any elements , the weight of their sum is the sum of their individual weights, i.e., .
Monotonicity of Weights ()
Let be an ordered real vector space and be its positive cone. For any weight , the map is monotone: for any , if , then . In other words, a larger outcome in the positive cone never receives a smaller weight.
A weight is faithful if
A weight on the positive cone is **faithful** if for every element , implies . Intuitively, this means that only the impossible outcome (the zero element) carries no weight at all.
Weight is finite
Let be an ordered real vector space and be its positive cone. A weight is said to be finite if for every outcome , the value assigned by the weight is not infinite, i.e., .
Weight is semifinite
A weight on the positive cone of an ordered real vector space is **semifinite** if for every non-zero outcome , there exists a non-zero outcome such that and its weight is finite (). This property ensures that every non-zero outcome, however large, contains some piece of finite weight.
Normality of a weight
A weight is said to be normal if it preserves the suprema of directed sets. Specifically, for any non-empty directed set that has a least upper bound , the weight of the supremum is the supremum of the weights: in the extended non-negative real numbers .
Mixture of weights
Given two weights on the positive cone and two non-negative real scalars , their mixture is the weight defined by for every . This operation results in a new weight that maps outcomes to values in the extended non-negative real numbers .
The value of the mixture at is
Let be an ordered real vector space and be its positive cone. For any weights on , non-negative real scalars , and any outcome , the value of the mixture of and with coefficients and applied to is equal to .
Certain outcome
The certain outcome is represented by the identity element of the space , considered as an element of the positive cone .
A convex combination of states is a state
Let be an ordered real vector space with an order unit . Suppose and are weights on the positive cone of that are states (meaning they are finite and normalized such that and ). For any non-negative real coefficients such that , the mixture is also a state.
Weight is finite
Let be an ordered real vector space and be its positive cone. A weight is finite (that is, for all ) if and only if its value at the order unit is finite, i.e., . This equivalence holds because the forward implication is immediate, and conversely, every element in the positive cone is bounded by a natural multiple of the order unit, allowing the finiteness to transfer via monotonicity.
Normalization of a weight
For a weight on the positive cone , its normalization is a weight defined by scaling by the multiplicative inverse of its value at the order unit . Specifically, for any element , the normalized weight assigns the value , where is considered the "mass" of the weight. This construction produces a normalized state when the mass is finite and non-zero.
Let be an ordered real vector space. For any weight on the positive cone and any element , the value of the normalized weight at is given by the formula where denotes the order unit in .
A Finite Weight with Non-zero Mass Normalizes to a State Weight
Let be an ordered real vector space and be a weight on the positive cone . If is finite (i.e., for all ) and its mass at the order unit is non-zero (), then the normalized weight is a state weight.
