StructuredSolvers

Implementation of solvers for composite optimization problems:

min_x f(x)+g(x)

The problems should implement the oracles for f and g described in the CompositeProblems.jl package.

Demo

julia> using StructuredSolvers, CompositeProblems
julia> n, m, sparsity = 10, 8, 0.8
julia> pb = get_logit_MLE(n=n, m=m, sparsity=0.8);
julia> x0 = zeros(n);

# Solving with proximal gradient
julia> optimizer = ProximalGradient()
julia> trace = optimize!(pb, optimizer, x0);

# Solving with proximal gradient
julia> optimizer = ProximalGradient(extrapolation = AcceleratedProxGrad())
julia> trace = optimize!(pb, optimizer, x0);

# Solving with Proximal Gradient and Riemannian Truncated Newton acceleration
julia> optimizer = PartlySmoothOptimizer(manifold_update = ManTruncatedNewton())
julia> trace = optimize!(pb, optimizer, x0, optparams = OptimizerParams(
        iterations_limit = 40,
        trace_length = 40,
    ));

Implemented solvers

Proximal Gradient (with backtracking line search)
- Vanilla proximal gradient optimizer = ProximalGradient()
- Accelerated proximal gradient optimizer = AcceleratedProximalGradient()
Structured solvers presented in this research paper with
- Riemannian Newton acceleration optimizer = PartlySmoothOptimizer(manifold_update = ManNewton());
- Riemannian Truncated Newton acceleration optimizer = PartlySmoothOptimizer(manifold_update = ManTruncatedNewton());

The Riemannian methods involve performing a Conjugate Gradient on the tangent space of the current iterate relative to the identified manifold, and a linesearch (backtracking from 1) to ensure descent.

Logging / trace functionality

Each algorithm should implement the display_logs function, which purpose is to display things if need be, and build an OptimizationState object instance. This OptimizationState is received by the generic optimize!, which adds it to the vector of past optimization states and eventually returns it.

The OptimizationState contains basic performance indicators. Specific ones can be added as a NamedTuple, whose template is provided to optimize! by the parameter optimstate_extensions, with an object like:

using StructuredSolvers, CompositeProblems
get_iterate(state::OptimizerState) = state.x
optimstate_extensions = [
    (
        key = :iterate,
        getvalue = get_iterate
    ),
]

trace = optimize!(pb, optimizer, x0, optimstate_extensions = optimstate_extensions)

Multiple dispatch allows to adapt the getter definition to the optimizer state if need be.

Timings

Method calls may be (roughly) timed using TimerOutputs. This functionality is turned on/off by calling TimerOutputs.enable_debug_timings(StructuredSolvers) and TimerOutputs.disable_debug_timings(StructuredSolvers).

Notes / TODOs