diff --git a/07_under_the_hood.md b/07_under_the_hood.md index e2e20e3..b40d9af 100644 --- a/07_under_the_hood.md +++ b/07_under_the_hood.md @@ -115,11 +115,12 @@ need to understand the constraint programming part of CP-SAT. ### What happens in CP-SAT on solve? -What actually happens when you execute `solver.Solve(model)`? +What actually happens when you execute `solver.solve(model)`? -1. The model is read. +1. The model is read from its protobuf representation. 2. The model is verified. -3. Preprocessing (multiple iterations): +3. Preprocessing (multiple iterations, you can control the number of iterations + with `max_presolve_iterations`): 1. Presolve (domain reduction) - Check [this video for SAT preprocessing](https://www.youtube.com/watch?v=ez9ArInp8w4), [this video for MaxSAT preprocessing](https://www.youtube.com/watch?v=xLg4hbM8ooM), @@ -130,7 +131,8 @@ What actually happens when you execute `solver.Solve(model)`? [FlatZinc and Flattening](https://www.minizinc.org/doc-2.5.5/en/flattening.html). 3. Detection of equivalent variables and [affine relations](https://personal.math.ubc.ca/~cass/courses/m309-03a/a1/olafson/affine_fuctions.htm). - 4. Substitute these by canonical representations + 4. Substitute these by canonical representations. Right now only affine + relations (a\*x + b = y) are supported. 5. Probe some variables to detect if they are actually fixed or detect further equivalences. 4. Load the preprocessed model into the underlying solver and create the linear @@ -138,7 +140,37 @@ What actually happens when you execute `solver.Solve(model)`? 5. **Search for solutions and bounds with the different solvers until the lower and upper bound match or another termination criterion is reached (e.g., time limit)** -6. Transform solution back to original model. + - A number of full subsolvers (aka complete strategies) will each block a + thread during the whole search. Each solver will use a different strategy, + mainly defined by some parameters, increasing the likelihood that one of + them will be the right choice for the problem at hand. For example, some + will work on a more linearized model, some will do more aggressive + restarts, some will focus on the lower bound, some on the upper bound, etc. + Theoretically, each of them should be able to find the optimal solution, + though, some will be much faster than others. + - Further, a number of first solution searchers will be started on the + remaining threads. These will be stopped as soon as a feasible solution is + found. + - As soon as a feasible solution is found, incomplete subsolvers take over + the free threads. These will try to improve the solution by using local + search heuristics, such as Large Neighborhood Search (LNS), which reuse a + lot of the techniques from the complete subsolvers. There are a lot of + different LNS strategies, which are applied via a Round Robin strategy. + Whenever, a worker thread finishes, it will perform an LNS iteration with + the next strategy in the list. In an LNS iteration it will: + 1. Take a solution from the pool of solutions. + 2. Remove a subset of variables from the solution. This subset is called + the neighborhood and its selection is one of the most important + differences between the different LNS strategies. + 3. Fix the other variables of the solution to their values. + 4. Presolve the model with the fixed variables. The fixations are likely to + significantly simplify the model. + 5. Solve the simplified model with complete strategy (as the full + subsolvers do) but isolated on this thread and with a very short time + limit. + 6. If a solution is found, it will be added to the pool of solutions. +6. Transform solution back to the original model, such that you can query the + values of the variables in your original model. This is taken from [this talk](https://youtu.be/lmy1ddn4cyw?t=434) and slightly extended. diff --git a/README.md b/README.md index 723d5b5..b066d16 100644 --- a/README.md +++ b/README.md @@ -4159,11 +4159,12 @@ need to understand the constraint programming part of CP-SAT. ### What happens in CP-SAT on solve? -What actually happens when you execute `solver.Solve(model)`? +What actually happens when you execute `solver.solve(model)`? -1. The model is read. +1. The model is read from its protobuf representation. 2. The model is verified. -3. Preprocessing (multiple iterations): +3. Preprocessing (multiple iterations, you can control the number of iterations + with `max_presolve_iterations`): 1. Presolve (domain reduction) - Check [this video for SAT preprocessing](https://www.youtube.com/watch?v=ez9ArInp8w4), [this video for MaxSAT preprocessing](https://www.youtube.com/watch?v=xLg4hbM8ooM), @@ -4174,7 +4175,8 @@ What actually happens when you execute `solver.Solve(model)`? [FlatZinc and Flattening](https://www.minizinc.org/doc-2.5.5/en/flattening.html). 3. Detection of equivalent variables and [affine relations](https://personal.math.ubc.ca/~cass/courses/m309-03a/a1/olafson/affine_fuctions.htm). - 4. Substitute these by canonical representations + 4. Substitute these by canonical representations. Right now only affine + relations (a\*x + b = y) are supported. 5. Probe some variables to detect if they are actually fixed or detect further equivalences. 4. Load the preprocessed model into the underlying solver and create the linear @@ -4182,7 +4184,37 @@ What actually happens when you execute `solver.Solve(model)`? 5. **Search for solutions and bounds with the different solvers until the lower and upper bound match or another termination criterion is reached (e.g., time limit)** -6. Transform solution back to original model. + - A number of full subsolvers (aka complete strategies) will each block a + thread during the whole search. Each solver will use a different strategy, + mainly defined by some parameters, increasing the likelihood that one of + them will be the right choice for the problem at hand. For example, some + will work on a more linearized model, some will do more aggressive + restarts, some will focus on the lower bound, some on the upper bound, etc. + Theoretically, each of them should be able to find the optimal solution, + though, some will be much faster than others. + - Further, a number of first solution searchers will be started on the + remaining threads. These will be stopped as soon as a feasible solution is + found. + - As soon as a feasible solution is found, incomplete subsolvers take over + the free threads. These will try to improve the solution by using local + search heuristics, such as Large Neighborhood Search (LNS), which reuse a + lot of the techniques from the complete subsolvers. There are a lot of + different LNS strategies, which are applied via a Round Robin strategy. + Whenever, a worker thread finishes, it will perform an LNS iteration with + the next strategy in the list. In an LNS iteration it will: + 1. Take a solution from the pool of solutions. + 2. Remove a subset of variables from the solution. This subset is called + the neighborhood and its selection is one of the most important + differences between the different LNS strategies. + 3. Fix the other variables of the solution to their values. + 4. Presolve the model with the fixed variables. The fixations are likely to + significantly simplify the model. + 5. Solve the simplified model with complete strategy (as the full + subsolvers do) but isolated on this thread and with a very short time + limit. + 6. If a solution is found, it will be added to the pool of solutions. +6. Transform solution back to the original model, such that you can query the + values of the variables in your original model. This is taken from [this talk](https://youtu.be/lmy1ddn4cyw?t=434) and slightly extended.