Alphabeta Math
AlgorithmAlgorithm: Literature-sourcedProof: Not applicablePipeline-generatedjudge pass (gpt-6-luna)
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Bias-corrected AMSGrad update

Definition

Fix a dimension d at least 1, an initial point x_1 in R^d, a constant step size eta>0, a denominator constant epsilon>0, and parameters beta_1,beta_2 in [0,1). Set m_0=v_0=\widehat v^{\max}_0=0. Given a gradient g_t in R^d at each integer time t>=1, calculate

mt=β1mt−1+(1−β1)gt,vt=β2vt−1+(1−β2)(gt⊙gt),

m^t=mt1−β1t,v^t=vt1−β2t,v^tmax⁡=max⁡(v^t−1max⁡,v^t),

where \odot and max are coordinatewise. Update

xt+1=xt−ηm^tv^tmax⁡+ϵ

coordinatewise. Each coordinate of v_t and \widehat v^{\max}_t is nonnegative, and the denominator is at least epsilon>0, so every finite update is defined.

This carrier defines a bias-corrected AMSGrad variant under the assigned denominator convention. Reddi–Kale–Kumar's Algorithm 2 takes the running maximum of the uncorrected second-moment recurrence and uses that maximum directly in the denominator; it does not apply Adam's second-moment bias correction. The recurrences are not identified here. No AMSGrad convergence theorem is claimed.

Remarks

For 0≤βj<1 and integer t≥1, Monotonicity of x↦xn and of n↦an gives 0≤βjt<1, so each bias-correction denominator is positive. Induction in the second-moment recurrence, Squares of nonzero elements are positive, Sign rules for products and monotonicity of multiplication and Multiplication by zero: 0⋅a=0 give vt≥0. Division by the positive bias-correction factor preserves nonnegativity by Inverses of positives are positive, and reciprocation reverses order, so v^t≥0 and its square root exists by Square roots exist: a unique a≥0 with (a)2=a; the positives are {x2:x≠0}. The coordinatewise maximum is the maximum of a two-element real set, which exists by Maximum and minimum of a set and Every nonempty finite set of reals has a maximum and a minimum and is nonnegative because it is one of its two nonnegative entries. Thus every denominator is at least ϵ>0.

Depends on

Used by

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