Implement constrained optimization and improve L-BFGS #151
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PatWie
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All function will receive the position to evaluate and return an entire
state (x, f(x), grad*, hessian*). This simplifies the interface, while
template parameters take care of treating functions according to
differentiability.
To support constrained problems, these are wrapped as an unconstrained
one via penalty method and augmented Lagrangian, so they can be solved
with any existing solver in this library.
It supports now:
min f(x)
s.t. c1(x) >= 0
c2(x) = 0
Several changes are made to L-BFGS for more robust numerical handling of
edge-cases.
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| #include <string> | ||
| #include <utility> | ||
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| #include "function.h" |
There was a problem hiding this comment.
[cpplint] reported by reviewdog 🐶
Include the directory when naming header files [build/include_subdir] [4]
| penalty = typename cfunction_t::scalar_t(10); | ||
| } | ||
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| ConstrainedState(const state_t &rhs) { CopyState(rhs); } // nolint |
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[cpplint] reported by reviewdog 🐶
Single-parameter constructors should be marked explicit. [runtime/explicit] [4]
| vector_t search_direction = current.gradient; | ||
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| constexpr scalar_t absolute_eps = 0.0001; | ||
| constexpr scalar_t eps = std::numeric_limits<scalar_t>::epsilon(); |
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[cpplint] reported by reviewdog 🐶
Add #include for numeric_limits<> [build/include_what_you_use] [4]
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All function will receive the position to evaluate and return an entire state (x, f(x), grad*, hessian*). This simplifies the interface, while template parameters take care of treating functions according to differentiability.
To support constrained problems, these are wrapped as an unconstrained one via penalty method and augmented Lagrangian, so they can be solved with any existing solver in this library.
It supports now:
min f(x)
s.t. c1(x) >= 0
c2(x) = 0
Several changes are made to L-BFGS for more robust numerical handling of edge-cases.