T adam 2026 - #368
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Introduce a new unconstrained optimizer, `tadam`, with a full `TADAMSolver` implementation, configurable parameter set, trust-region step logic, momentum/RMS updates, and solver-specific stats (`avgβ1max`). Wire it into `JSOSolvers.jl` via `include("tadam.jl")`.
Also add a standalone `tadam_example` environment and test script that sets up a Rastrigin problem and compares TADAM against LBFGS for experimentation.
Move gradient evaluation and direction update to the start of each solver loop so the subproblem always uses a fresh direction, even on rejected steps. Dual residual stats are now updated every iteration, and redundant `d` recomputations in branch blocks were removed. The bias-correction denominator for `d` now uses `max(1, siter)` to avoid the `siter=0` edge case.
Refactors the TADAM iteration flow so step computation, acceptance, callback batch advancement, and next-direction preparation happen in a consistent order. Momentum/variance absorption now occurs only after accepted steps, and the next gradient/beta geometry is computed on the new batch after the callback. Rejected steps now reuse the current gradient/momentum and only recompute direction with a shrunk β1max. Also adds a ΔTk <= eps(T) guard to prevent Float32 underflow when computing ρk.
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## main #368 +/- ##
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- Coverage 89.00% 76.19% -12.81%
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Files 7 8 +1
Lines 1200 1420 +220
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+ Hits 1068 1082 +14
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d-monnet
reviewed
Jul 24, 2026
| Analytically finds the largest τ ∈ [0,1] satisfying both gradient-related conditions. | ||
| """ | ||
| function find_tau(sc::V, s_infty::V, g::V, κ1::T, κ2::T) where {V, T} | ||
| w = s_infty .- sc |
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there is memory allocation here, this is no good
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