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Equation 19 · Part 6 · Shrink It, Train It Small, or Search for It: The Main Strategies for Small Models, Compared

Symbol g

a∗=arg⁡min⁡a∈A Lval(w∗(a), a)subject tog(a)≤B,w∗(a)=arg⁡min⁡w Ltrain(w,a)a^* = \arg\min_{a \in \mathcal{A}} \ \mathcal{L}_{\text{val}}\big(w^*(a),\, a\big) \quad \text{subject to} \quad g(a) \le B, \qquad w^*(a) = \arg\min_{w} \ \mathcal{L}_{\text{train}}(w, a)
gg

What this part means

g is one factor in the product that computes the quantity on the left.

Its job in the formula

g is one factor in the product that computes the quantity on the left.

The passage around this formula

…the controller [ 7 ] . Formally, a NAS run of this kind is a constrained, nested optimization: a∗=arg⁡min⁡a∈A Lval(w∗(a), a)subject tog(a)≤B,w∗(a)=arg⁡min⁡w Ltrain(w,a)a^* = \arg\min_{a \in \mathcal{A}} \ \mathcal{L}_{\text{val}}\big(w^*(a),\, a\big) \quad \text{subject to} \quad g(a) \le B, \qquad w^*(a) = \arg\min_{w} \ \mathcal{L}_{\text{train}}(w, a). where A\mathcal{A} is a search space of candidate architectures designed in advance by the researchers, g(a) some measured deployment cost of architecture a , and B a budget the target device imposes. Every term in that equation is a documented design choice, and the choice of g turns out to be where the edge-specific literature does its most important work.

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Learn the underlying idea

A function assigns an output to each allowed input. The expression f(x) means “apply f to x”.

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Sources cited in the surrounding passage

These citations provide research context; check each source for the exact claim it supports.