Commit e302a61e authored by Davis King's avatar Davis King

Clarified spec

parent 89d3fe4e
...@@ -114,7 +114,11 @@ namespace dlib ...@@ -114,7 +114,11 @@ namespace dlib
elsewhere rather than on further improving the current local optima found so elsewhere rather than on further improving the current local optima found so
far. That is, once a local maxima is identified to about solver_epsilon far. That is, once a local maxima is identified to about solver_epsilon
accuracy, the algorithm will spend all its time exploring the functions to accuracy, the algorithm will spend all its time exploring the functions to
find other local maxima to investigate. find other local maxima to investigate. An epsilon of 0 means it will keep
solving until it reaches full floating point precision. Larger values will
cause it to switch to pure global exploration sooner and therefore might be
more effective if your objective function has many local maxima and you don't
care about a super high precision solution.
- find_max_global() runs until one of the following is true: - find_max_global() runs until one of the following is true:
- The total number of calls to the provided functions is == num.max_calls - The total number of calls to the provided functions is == num.max_calls
- More than max_runtime time has elapsed since the start of this function. - More than max_runtime time has elapsed since the start of this function.
...@@ -178,7 +182,11 @@ namespace dlib ...@@ -178,7 +182,11 @@ namespace dlib
elsewhere rather than on further improving the current local optima found so elsewhere rather than on further improving the current local optima found so
far. That is, once a local maxima is identified to about solver_epsilon far. That is, once a local maxima is identified to about solver_epsilon
accuracy, the algorithm will spend all its time exploring the function to accuracy, the algorithm will spend all its time exploring the function to
find other local maxima to investigate. find other local maxima to investigate. An epsilon of 0 means it will keep
solving until it reaches full floating point precision. Larger values will
cause it to switch to pure global exploration sooner and therefore might be
more effective if your objective function has many local maxima and you don't
care about a super high precision solution.
- find_max_global() runs until one of the following is true: - find_max_global() runs until one of the following is true:
- The total number of calls to f() is == num.max_calls - The total number of calls to f() is == num.max_calls
- More than max_runtime time has elapsed since the start of this function. - More than max_runtime time has elapsed since the start of this function.
......
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