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Coordinate Ascent Solver

Source: R/solver_coordinate.R
coordinate_ascent.Rd

Creates a solver that optimizes one parameter at a time while holding others fixed. This is useful when parameters have different scales or when the likelihood decomposes nicely along coordinate directions.

Usage

coordinate_ascent(
  max_cycles = 50L,
  tol = 1e-08,
  line_search = TRUE,
  cycle_order = c("sequential", "random"),
  verbose = FALSE
)

Arguments

max_cycles

Maximum number of full cycles through all parameters

tol

Convergence tolerance on log-likelihood change

Use line search for each coordinate (slower but more robust)

cycle_order

Order of cycling: "sequential" (1,2,...,p) or "random"

verbose

Logical; if TRUE and the cli package is installed, display progress during optimization. Default is FALSE.

Value

A solver function with signature (problem, theta0, trace) -> mle_result

Details

Each cycle consists of optimizing each coordinate in turn using a simple golden section search. The algorithm converges when the log-likelihood improvement in a full cycle is less than tol.

Coordinate ascent can be effective when:

  • Parameters are on very different scales

  • The likelihood has axis-aligned ridges

  • Computing the full gradient is expensive

However, it may converge slowly for problems with strong parameter correlations.

Examples

# Basic coordinate ascent
solver <- coordinate_ascent()

# With more cycles for difficult problems
solver <- coordinate_ascent(max_cycles = 100)

# Random cycling to avoid systematic bias
solver <- coordinate_ascent(cycle_order = "random")