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Type Alias: Parameters

Parameters: { absoluteGapTolerance: number; allocationBlockSize: number; color: "Never" | "Auto" | "Always"; cumulPropagationLevel: number; fdsAdditionalStepRatio: number; fdsBothFailRewardFactor: number; fdsBranchOnObjective: boolean; fdsBranchOrdering: "FailureFirst" | "FailureLast" | "Random"; fdsDualResetRatings: boolean; fdsDualStrategy: "Minimum" | "Random" | "Split"; fdsEpsilon: number; fdsEventTimeInfluence: number; fdsFixedAlpha: number; fdsInitialRating: number; fdsInitialRestartLimit: number; fdsLengthStepRatio: number; fdsMaxCounterAfterRestart: number; fdsMaxCounterAfterSolution: number; fdsMaxInitialChoicesPerVariable: number; fdsMaxInitialLengthChoices: number; fdsMinIntVarChoiceStep: number; fdsMinLengthChoiceStep: number; fdsPresenceStatusChoices: boolean; fdsRatingAverageComparison: "Off" | "Global" | "Depth"; fdsRatingAverageLength: number; fdsReductionFactor: "Normal" | "Zero" | "Random"; fdsReductionWeight: number; fdsResetRestartsAfterSolution: boolean; fdsRestartGrowthFactor: number; fdsRestartStrategy: "Geometric" | "Nested" | "Luby"; fdsReuseClosing: boolean; fdsStrongBranchingCriterion: "Both" | "Left" | "Right"; fdsStrongBranchingDepth: number; fdsStrongBranchingSize: number; fdsUniformChoiceStep: boolean; fdsUseNogoods: boolean; infoTraceLevel: number; integralPropagationLevel: number; lnsUseWarmStartOnly: boolean; logLevel: number; logPeriod: number; nbWorkers: number; noOverlapPropagationLevel: number; positionPropagationLevel: number; preset: "Auto" | "Default" | "Large"; printLog: NodeJS.WritableStream | boolean; processExitTimeout: number; propagationTraceLevel: number; randomSeed: number; relativeGapTolerance: number; reservoirPropagationLevel: number; searchTraceLevel: number; searchType: "Auto" | "LNS" | "FDS" | "FDSDual" | "SetTimes" | "FDSLB"; simpleLBMaxIterations: number; simpleLBShavingRounds: number; simpleLBWorker: number; solutionLimit: number; solver: string; solverArgs: string[]; timeLimit: number; usePrecedenceEnergy: number; verifyExternalSolutions: boolean; verifySolutions: boolean; warningLevel: number; workers: WorkerParameters[]; }

Type declaration​

Parameters​

solver?​

optional solver: string

Path to the solver executable or WebSocket URL.

Remarks​

Specifies how to connect to the solver.

  • Local path: Path to the optalcp executable (e.g., /usr/bin/optalcp). The API spawns the solver as a subprocess.
  • WebSocket URL: URL starting with ws://, wss://, http://, or https:// (e.g., ws://localhost:8080). The API connects via WebSocket to a remote solver. In browser environments, a WebSocket URL is required since browsers cannot spawn local processes.

If not specified, the solver is searched as described in Solver.findSolver.

See​

solverArgs?​

optional solverArgs: string[]

Additional command-line arguments for the solver subprocess.

Remarks​

These arguments are passed directly to the solver subprocess when it is spawned. This parameter is only used in subprocess mode (not when connecting to a remote solver via WebSocket).

This can be useful for debugging or passing special flags to the solver that are not exposed through the Parameters API.

import * as cp from '@scheduleopt/optalcp';

const model = new cp.Model();
// ... build model ...

// Pass custom arguments to the solver
const result = await model.solve({
  solverArgs: ['--some-debug-flag'],
  timeLimit: 60
});
See​

Parameters.solver to specify a custom solver path.

workers?​

optional workers: WorkerParameters[]

Per-worker parameter overrides.

Remarks​

Each worker can have its own parameters. If a parameter is not specified for a worker, then the global value is used.

Note that parameter Parameters.nbWorkers specifies the number of workers regardless of the length of this list.

See​

WorkerParameters for the list of parameters that can be set per worker.

fdsAdditionalStepRatio?​

optional fdsAdditionalStepRatio: number

Domain split ratio when run out of choices

Remarks​

When all choices are decided, and a greedy algorithm cannot find a solution, then more choices are generated by splitting domains into the specified number of pieces.

The parameter takes a floating point value in range 2.0..Infinity.

The default value is 7.

fdsBothFailRewardFactor?​

optional fdsBothFailRewardFactor: number

How much to improve rating when both branches fail immediately

Remarks​

This parameter sets a bonus reward for a choice when both left and right branches fail immediately. Current rating of both branches is multiplied by the specified value.

The parameter takes a floating point value in range 0..1.

The default value is 0.98.

fdsBranchOnObjective?​

optional fdsBranchOnObjective: boolean

Whether to generate choices for objective expression/variable

Remarks​

This option controls the generation of choices on the objective. It works regardless of the objective is given by an expression or a variable.

The default value is False.

fdsBranchOrdering?​

optional fdsBranchOrdering: "FailureFirst" | "FailureLast" | "Random"

Controls which side of a choice is explored first (considering the rating).

Remarks​

This option can take the following values:

  • FailureFirst: Explore the failure side first.
  • FailureLast: Explore the failure side last.
  • Random: Explore either side randomly.

The default value is FailureFirst.

fdsDualResetRatings?​

optional fdsDualResetRatings: boolean

Whether to reset ratings when a new LB is proved

Remarks​

When this parameter is on, and FDSDual proves a new lower bound, then all ratings are reset to default values.

The default value is False.

fdsDualStrategy?​

optional fdsDualStrategy: "Minimum" | "Random" | "Split"

A strategy to choose objective cuts during FDSDual search.

Remarks​

Possible values are:

  • Minimum: Always change the cut by the minimum amount.
  • Random: At each restart, randomly choose a value in range LB..UB. The default.
  • Split: Always split the current range LB..UB in half.

The default value is Random.

fdsEpsilon?​

optional fdsEpsilon: number

How often to chose a choice randomly

Remarks​

Probability that a choice is taken randomly. A randomly selected choice is not added to the search tree automatically. Instead, the choice is tried, its rating is updated, but it is added to the search tree only if one of the branches fails. The mechanism is similar to strong branching.

The parameter takes a floating point value in range 0.0..0.99999.

The default value is 0.1.

fdsEventTimeInfluence?​

optional fdsEventTimeInfluence: number

Influence of event time to initial choice rating

Remarks​

When non-zero, the initial choice rating is influenced by the date of the choice. This way, very first choices in the search should be taken chronologically.

The parameter takes a floating point value in range 0..1.

The default value is 0.

fdsFixedAlpha?​

optional fdsFixedAlpha: number

When non-zero, alpha factor for rating updates

Remarks​

When this parameter is set to a non-zero, parameter FDSRatingAverageLength is ignored. Instead, the rating of a branch is computed as an exponential moving average with the given parameter alpha.

The parameter takes a floating point value in range 0..1.

The default value is 0.

fdsInitialRating?​

optional fdsInitialRating: number

Initial rating for newly created choices

Remarks​

Default rating for newly created choices. Both left and right branches get the same rating. Choice is initially permuted so that bigger domain change is the left branch.

The parameter takes a floating point value in range 0.0..2.0.

The default value is 0.5.

fdsInitialRestartLimit?​

optional fdsInitialRestartLimit: number

Fail limit for the first restart

Remarks​

Failure-directed search is periodically restarted: explored part of the current search tree is turned into a no-good constraint, and the search starts again in the root node. This parameter specifies the size of the very first search tree (measured in number of failures).

The parameter takes an integer value in range 1..9223372036854775807.

The default value is 100.

fdsLengthStepRatio?​

optional fdsLengthStepRatio: number

Choice step relative to average length

Remarks​

Ratio of initial choice step size to the minimum length of interval variable. When FDSUniformChoiceStep is set, this ratio is used to compute global choice step using the average of interval var length. When FDSUniformChoiceStep is not set, this ratio is used to compute the choice step for every interval var individually.

The parameter takes a floating point value in range 0.0..Infinity.

The default value is 0.699999988079071.

fdsMaxCounterAfterRestart?​

optional fdsMaxCounterAfterRestart: number

Truncate choice use counts after a restart to this value

Remarks​

The idea is that ratings learned in the previous restart are less valid in the new restart. Using this parameter, it is possible to truncate use counts on choices so that new local ratings will have bigger weights (when FDSFixedAlpha is not used).

The parameter takes an integer value.

The default value is 255.

fdsMaxCounterAfterSolution?​

optional fdsMaxCounterAfterSolution: number

Truncate choice use counts after a solution is found

Remarks​

Similar to Parameters.fdsMaxCounterAfterRestart, this parameter allows truncating use counts on choices when a solution is found.

The parameter takes an integer value.

The default value is 255.

fdsMaxInitialChoicesPerVariable?​

optional fdsMaxInitialChoicesPerVariable: number

Maximum number of choices generated initially per a variable

Remarks​

Initial domains are often very large (e.g., 0..IntervalMax). Therefore initial number of generated choices is limited: only choices near startMin are kept.

The parameter takes an integer value in range 2..2147483647.

The default value is 90.

fdsMaxInitialLengthChoices?​

optional fdsMaxInitialLengthChoices: number

Maximum number of initial choices on length of an interval variable

Remarks​

When non-zero, this parameter limits the number of initial choices generated on length of an interval variable. When zero (the default), no choices on length are generated.

The parameter takes an integer value in range 0..2147483647.

The default value is 0.

fdsMinIntVarChoiceStep?​

optional fdsMinIntVarChoiceStep: number

Minimum step when generating choices for integer variables.

Remarks​

Steps between choices for integer variables are never smaller than the specified value.

The parameter takes an integer value in range 1..1073741823.

The default value is 1073741823.

fdsMinLengthChoiceStep?​

optional fdsMinLengthChoiceStep: number

Maximum step when generating initial choices for length of an interval variable

Remarks​

Steps between choices for length of an interval variable are never bigger than the specified value.

The parameter takes an integer value in range 1..1073741823.

The default value is 1073741823.

fdsPresenceStatusChoices?​

optional fdsPresenceStatusChoices: boolean

Whether to generate choices on presence status

Remarks​

Choices on start time also include a choice on presence status. Therefore, dedicated choices on presence status only are not mandatory.

The default value is True.

fdsRatingAverageComparison?​

optional fdsRatingAverageComparison: "Off" | "Global" | "Depth"

Whether to compare the local rating with the average

Remarks​

Possible values are:

  • Off (the default): No comparison is done.
  • Global: Compare with the global average.
  • Depth: Compare with the average on the current search depth

Arithmetic average is used for global and depth averages.

The default value is Off.

fdsRatingAverageLength?​

optional fdsRatingAverageLength: number

Length of average rating computed for choices

Remarks​

For the computation of rating of a branch. Arithmetic average is used until the branch is taken at least FDSRatingAverageLength times. After that exponential moving average is used with parameter alpha = 1 - 1 / FDSRatingAverageLength.

The parameter takes an integer value in range 0..254.

The default value is 25.

fdsReductionFactor?​

optional fdsReductionFactor: "Normal" | "Zero" | "Random"

Reduction factor R for rating computation

Remarks​

Possible values are:

  • Normal (the default): Normal reduction factor.
  • Zero: Factor is not used (it is 0 all the time).
  • Random: A random number in the range [0,1] is used instead.

The default value is Normal.

fdsReductionWeight?​

optional fdsReductionWeight: number

Weight of the reduction factor in rating computation

Remarks​

When computing the local rating of a branch, multiply reduction factor by the given weight.

The parameter takes a floating point value in range 0.0..Infinity.

The default value is 1.

fdsResetRestartsAfterSolution?​

optional fdsResetRestartsAfterSolution: boolean

Reset restart size after a solution is found (ignored in Luby)

Remarks​

When this parameter is set (the default), then restart limit is set back to Parameters.fdsInitialRestartLimit when a solution is found.

The default value is True.

fdsRestartGrowthFactor?​

optional fdsRestartGrowthFactor: number

Growth factor for fail limit after each restart

Remarks​

After each restart, the fail limit for the restart is multiplied by the specified factor. This parameter is ignored when Parameters.fdsRestartStrategy is Luby.

The parameter takes a floating point value in range 1.0..Infinity.

The default value is 1.15.

fdsRestartStrategy?​

optional fdsRestartStrategy: "Geometric" | "Nested" | "Luby"

Restart strategy to use

Remarks​

This parameter specifies how the restart limit (maximum number of failures) changes from restart to restart. Possible values are:

The default value is Geometric.

fdsReuseClosing?​

optional fdsReuseClosing: boolean

Whether always reuse closing choice

Remarks​

Most of the time, FDS reuses closing choice automatically. This parameter enforces it all the time.

The default value is False.

fdsStrongBranchingCriterion?​

optional fdsStrongBranchingCriterion: "Both" | "Left" | "Right"

How to choose the best choice in strong branching

Remarks​

Possible values are:

  • Both: Choose the the choice with best combined rating.
  • Left (the default): Choose the choice with the best rating of the left branch.
  • Right: Choose the choice with the best rating of the right branch.

The default value is Left.

fdsStrongBranchingDepth?​

optional fdsStrongBranchingDepth: number

Up-to what search depth apply strong branching

Remarks​

Strong branching is typically used in the root node. This parameter controls the maximum search depth when strong branching is used.

The parameter takes an integer value.

The default value is 6.

fdsStrongBranchingSize?​

optional fdsStrongBranchingSize: number

Number of choices to try in strong branching

Remarks​

Strong branching means that instead of taking a choice with the best rating, we take the specified number (FDSStrongBranchingSize) of best choices, try them in dry-run mode, measure their local rating, and then chose the one with the best local rating.

The parameter takes an integer value.

The default value is 10.

fdsUniformChoiceStep?​

optional fdsUniformChoiceStep: boolean

Whether all initial choices have the same step length

Remarks​

When set, then initial choices generated on interval variables will have the same step size.

The default value is True.

fdsUseNogoods?​

optional fdsUseNogoods: boolean

Whether to use or not nogood constraints

Remarks​

By default, no-good constraint is generated after each restart. This parameter allows to turn no-good constraints off.

The default value is True.

Gap Tolerance​

absoluteGapTolerance?​

optional absoluteGapTolerance: number

Stop the search when the gap is below the tolerance

Remarks​

The search is stopped if the absolute difference between the current solution value and current lower/upper bound is not bigger than the specified value.

This parameter works together with Parameters.relativeGapTolerance as an OR condition: the search stops when either the absolute gap or the relative gap is within tolerance.

The parameter takes a floating point value.

The default value is 0.

relativeGapTolerance?​

optional relativeGapTolerance: number

Stop the search when the gap is below the tolerance

Remarks​

The search is stopped if the relative difference between the current solution value and current lower/upper bound is not bigger than the specified value.

This parameter works together with Parameters.absoluteGapTolerance as an OR condition: the search stops when either the absolute gap or the relative gap is within tolerance.

The parameter takes a floating point value.

The default value is 0.0001.

lnsUseWarmStartOnly?​

optional lnsUseWarmStartOnly: boolean

Use only the user-provided warm start as the initial solution in LNS

Remarks​

When this parameter is on, the solver will use only the user-specified warm start solution for the initial solution phase in LNS. If no warm start is provided, the solver will search for its own initial solution as usual.

The default value is False.

Limits​

solutionLimit?​

optional solutionLimit: number

Stop the search after the given number of solutions

Remarks​

Terminates the solve after the specified number of solutions have been found and reported.

Automatic behavior (value 0):

When set to 0 (the default), the limit is determined automatically based on the problem type:

  • Decision problems (no objective): The solver stops after finding the first solution. This is usually what you want for feasibility problems.

  • Optimization problems: No limit is applied. The solver continues searching for better solutions until it proves optimality, hits another limit (like Parameters.timeLimit), or is stopped manually.

Explicit values:

You can set an explicit limit to control solution enumeration:

  • 1: Stop after the first solution. Useful when you just need any feasible solution quickly, even for optimization problems.

  • N > 1: Find up to N solutions. Useful for:

    • Generating multiple alternative solutions for warm starts
    • Enumerating all solutions to small problems
    • Finding a diverse set of solutions for analysis

Note on optimization problems:

For optimization problems, only improving solutions are counted. If you set solutionLimit=5, the solver will stop after finding 5 solutions, each better than the previous. Non-improving solutions (which can occur during the search) are not counted toward the limit.

Note on LNS and decision problems:

When using LNS search (see Parameters.searchType) on decision problems (no objective), be aware that LNS may report duplicate solutions. LNS works by iteratively improving a solution, and for decision problems without an objective to guide the search, it may find the same solution multiple times. If you need unique solutions, consider using FDS search instead, or filter duplicates in your application code.

import * as CP from "optalcp";

let model = new CP.Model();
// ... build your model ...

// Automatic behavior (default)
// - Decision problem: stops after 1 solution
// - Optimization: no limit
let result = await model.solve();

// Stop after first solution (useful for quick feasibility check)
result = await model.solve({ solutionLimit: 1 });

// Find up to 10 solutions for warm starts
result = await model.solve({ solutionLimit: 10 });
See​

Parameters.timeLimit for limiting solve time.

timeLimit?​

optional timeLimit: number

Wall clock limit for execution in seconds

Remarks​

Caps the total wall-clock time spent by the solver. The timer starts as soon as the solve begins, and it includes presolve, search, and verification. When the limit is reached, all workers stop cooperatively. Leave it at the default Infinity to run without a time bound.

The parameter takes a floating point value in range 0.0..Infinity.

The default value is Infinity.

Major options​

allocationBlockSize?​

optional allocationBlockSize: number

The minimal amount of memory in kB for a single allocation

Remarks​

The solver allocates memory in blocks. This parameter sets the minimal size of a block. Larger blocks mean a higher risk of wasting memory. However, larger blocks may also lead to better performance, particularly when the size matches the page size supported by the operating system.

The value of this parameter must be a power of 2.

The default value of 2048 means 2MB, which means that up to ~12MB can be wasted per worker in the worst case.

The parameter takes an integer value in range 4..1073741824.

The default value is 2048.

logLevel?​

optional logLevel: number

Level of the log

Remarks​

This parameter controls the amount of text the solver writes on standard output. The solver is completely silent when this option is set to 0.

The parameter takes an integer value in range 0..3.

The default value is 2.

logPeriod?​

optional logPeriod: number

How often to print log messages (in seconds)

Remarks​

When Parameters.logLevel ≥ 2 then solver writes a log message every logPeriod seconds. The log message contains the current statistics about the solve: number of branches, number of fails, memory used, etc.

The parameter takes a floating point value in range 0.01..Infinity.

The default value is 10.

nbWorkers?​

optional nbWorkers: number

Number of threads dedicated to search

Remarks​

When this parameter is 0 (the default), the number of workers is determined the following way:

  • If environment variable OPTALCP_NB_WORKERS is set, its value is used.
  • Otherwise, all available cores are used.

The parameter takes an integer value.

The default value is 0.

preset?​

optional preset: "Auto" | "Default" | "Large"

Preset configuration for solver parameters

Remarks​

Presets provide reasonable default values for multiple solver parameters at once. Instead of manually tuning individual parameters, you can select a preset that matches your problem characteristics. The solver will then configure search strategies and propagation levels appropriately.

Available presets:

  • Auto: The solver automatically selects a preset based on problem size (the default). Problems with more than 100,000 variables use Large, otherwise Default.

  • Default: Balanced configuration for most problems. Uses maximum propagation levels and distributes workers across different search strategies: half use LNS, 3/8 use FDS, and the rest use FDSDual. This provides a good mix of exploration and exploitation.

  • Large: Optimized for big problems with more than 100,000 variables. Uses minimum propagation to reduce overhead, and all workers use LNS search. This trades propagation strength for scalability.

Parameters affected by presets:

The preset sets default values for the following parameters:

When you explicitly set any of these parameters, your value takes precedence over the preset's default. This allows you to use a preset as a starting point and fine-tune specific parameters as needed.

When to use presets:

Presets are a good starting point for most problems. They are not guaranteed to be optimal for your specific problem, but they provide reasonable defaults that work well in practice. If you find that the default preset is not working well for your problem, consider:

  • Trying the Large preset for very big problems, even if they have fewer than 100,000 variables
  • Explicitly setting Parameters.searchType to use a specific search strategy
  • Adjusting propagation levels based on your problem structure
import * as CP from "optalcp";

let model = new CP.Model();
// ... build your model ...

// Use automatic preset selection
let result = await model.solve();

// Or explicitly select a preset for a large problem
result = await model.solve({ preset: "Large" });

// Or use Default preset but override search type
result = await model.solve({ preset: "Default", searchType: "FDS" });
See​

processExitTimeout?​

optional processExitTimeout: number

Timeout for solver process to exit after finishing

Remarks​

After the solver finishes, wait up to this many seconds for the process to exit. If it doesn't exit in time, it is silently killed.

The parameter takes a floating point value in range 0.0..Infinity.

The default value is 3.

randomSeed?​

optional randomSeed: number

Random seed

Remarks​

The solver breaks ties randomly using a pseudorandom number generator. This parameter sets the seed of the generator.

Note that when Parameters.nbWorkers is more than 1 then there is also another source of randomness: the time it takes for a message to pass from one worker to another. Therefore with 1 worker the solver is deterministic (random behavior depends only on random seed). With more workers the solver is not deterministic.

Even with the same random seed, the solver may behave differently on different platforms. This can be due to different implementations of certain functions such as std::sort.

The parameter takes an integer value.

The default value is 1.

searchType?​

optional searchType: "Auto" | "LNS" | "FDS" | "FDSDual" | "SetTimes" | "FDSLB"

Type of search to use

Remarks​

This parameter controls which search algorithm the solver uses. Different search types have different strengths:

  • Auto: Automatically determined based on the Parameters.preset (the default). With the Default preset, workers are distributed across LNS, FDS, and FDSDual. With the Large preset, all workers use LNS.

  • LNS: Large Neighborhood Search. Starts from an initial solution and iteratively improves it by relaxing and re-optimizing parts of the solution. Good for finding high-quality solutions quickly, especially on large problems. Works best when a good initial solution can be found.

  • FDS: Failure-Directed Search. A systematic search that learns from failures to guide exploration. Uses restarts with no-good learning. Often effective at proving optimality and works well with strong propagation.

  • FDSDual: Failure-Directed Search working on objective bounds. Similar to FDS but focuses on proving bounds on the objective value. Useful for optimization problems where you want to know how far from optimal your solutions are.

  • SetTimes: Depth-first set-times search (not restarted). A simple chronological search that assigns start times in order. Can be effective for tightly constrained problems but generally less robust than other methods.

Interaction with presets:

When searchType is set to Auto, the actual search type is determined by the Parameters.preset:

  • Default preset: Distributes workers across different search types. Half use LNS, 3/8 use FDS, and the rest use FDSDual. This portfolio approach provides robustness across different problem types.

  • Large preset: All workers use LNS. For very large problems, the overhead of systematic search methods like FDS becomes prohibitive, so LNS is used exclusively.

If you explicitly set searchType to a specific value (not Auto), that value is used regardless of the preset.

import * as CP from "optalcp";

let model = new CP.Model();
// ... build your model ...

// Let the preset decide (default behavior)
let result = await model.solve();

// Or explicitly use FDS for systematic search
result = await model.solve({ searchType: "FDS" });

// Or use LNS for quick solutions on large problems
result = await model.solve({ searchType: "LNS" });
See​

verifyExternalSolutions?​

optional verifyExternalSolutions: boolean

Whether to verify correctness of external solutions

Remarks​

External solutions can be passed to the solver as a warm start via Model.solve, or using Solver.sendSolution during the search. Normally, all external solutions are checked before they are used. However, the check may be time consuming, especially if too many external solutions are sent simultaneously. This parameter allows to turn the check off.

The default value is True.

verifySolutions?​

optional verifySolutions: boolean

When on, the correctness of solutions is verified

Remarks​

Verification is an independent algorithm that checks whether all constraints in the model are satisfied (or absent), and that objective value was computed correctly. Verification is a somewhat redundant process as all solutions should be correct. Its purpose is to double-check and detect bugs in the solver.

The default value is False.

warningLevel?​

optional warningLevel: number

Level of warnings

Remarks​

This parameter controls the types of warnings the solver emits. When this parameter is set to 0 then no warnings are emitted.

The parameter takes an integer value in range 0..3.

The default value is 2.

Other​

printLog?​

optional printLog: NodeJS.WritableStream | boolean

Where to write solver log output.

Remarks​

Controls where solver log messages, warnings, and errors are written during solving.

Node.js:

  • undefined (default): Write to console (process.stdout)
  • false: Suppress all output
  • true: Write to console (explicit)
  • WritableStream: Write to the provided stream

Browser:

  • undefined (default): Silent (no output)
  • false: Silent
  • true: Write to browser console

Note that setting printLog to false only suppresses writing to the output stream. The solver still emits log, warning, and error events that can be intercepted using callback properties (Solver.onLog, Solver.onWarning, Solver.onError). To reduce the amount of logging at the source, use Parameters.logLevel.

ANSI colors: When writing to a stream, the solver automatically detects whether the stream supports colors by checking its isTTY property. In browser environment, colors are always disabled. To override automatic detection, use the Parameters.color parameter.

If the output stream becomes non-writable (e.g., a broken pipe), then the solver stops as soon as possible.

// Default - Node.js logs to console, browser is silent
await model.solve();

// Silent - no output
await model.solve({ printLog: false });

// Console output (useful in browser)
await model.solve({ printLog: true });

// Custom stream (Node.js only)
import * as fs from 'fs';
await model.solve({ printLog: fs.createWriteStream('solver.log') });
See​

Propagation levels​

cumulPropagationLevel?​

optional cumulPropagationLevel: number

How much to propagate constraints on cumul functions

Remarks​

This parameter controls the amount of propagation done for CumulExpr.le constraint when used with a sum of Model.pulse pulses.

Higher levels use more sophisticated algorithms that can detect more infeasibilities and prune more values from domains, but at the cost of increased computation time.

Propagation levels:

  • Level 1: Basic timetable propagation
  • Level 2: Adds time-table edge-finding
  • Level 3: Maximum propagation with all available algorithms

Automatic selection (level 0):

When set to 0 (the default), the propagation level is determined automatically based on the Parameters.preset:

  • Default preset: Uses level 3 (maximum propagation)
  • Large preset: Uses level 1 (minimum propagation for scalability)

Performance considerations:

More propagation doesn't necessarily mean better overall performance. The trade-off depends on your problem:

  • Resource-constrained problems with tight capacity limits often benefit from higher propagation levels because cumulative reasoning can prune many infeasible assignments.

  • Problems with loose resource constraints may not benefit much from higher levels because the extra computation doesn't lead to significant pruning.

  • Very large problems may perform better with lower propagation levels because the overhead becomes prohibitive.

  • FDS search (see Parameters.searchType) typically benefits from higher propagation levels.

If you're unsure, start with the automatic selection (level 0) and let the preset choose.

import * as CP from "optalcp";

let model = new CP.Model();
// ... build your model with cumulative constraints ...

// Let the preset decide (default)
let result = await model.solve();

// Or use maximum propagation for resource-constrained problems
result = await model.solve({ cumulPropagationLevel: 3 });

// Or use minimum propagation for very large problems
result = await model.solve({ cumulPropagationLevel: 1 });
See​

integralPropagationLevel?​

optional integralPropagationLevel: number

How much to propagate integral expression

Remarks​

This parameter controls the amount of propagation done for Model.integral expressions. In particular, it controls whether the propagation also affects the minimum and the maximum length of the associated interval variable:

  • 1: The length is updated only once during initial constraint propagation.
  • 2: The length is updated every time the expression is propagated.

The parameter takes an integer value in range 1..2.

The default value is 1.

noOverlapPropagationLevel?​

optional noOverlapPropagationLevel: number

How much to propagate noOverlap constraints

Remarks​

This parameter controls the amount of propagation done for noOverlap constraints. Higher levels use more sophisticated algorithms that can detect more infeasibilities and prune more values from domains, but at the cost of increased computation time.

Propagation levels:

  • Level 1: Basic timetable propagation only
  • Level 2: Adds detectable precedences algorithm
  • Level 3: Adds edge-finding reasoning
  • Level 4: Maximum propagation with all available algorithms

Automatic selection (level 0):

When set to 0 (the default), the propagation level is determined automatically based on the Parameters.preset:

  • Default preset: Uses level 4 (maximum propagation)
  • Large preset: Uses level 1 (minimum propagation for scalability)

Performance considerations:

More propagation doesn't necessarily mean better overall performance. The trade-off depends on your problem:

  • Dense scheduling problems with many overlapping intervals often benefit from higher propagation levels because the extra pruning reduces the search space significantly.

  • Sparse problems or very large problems may perform better with lower propagation levels because the overhead of sophisticated algorithms outweighs the benefit.

  • FDS search (see Parameters.searchType) typically benefits from higher propagation levels because it relies on strong propagation to guide the search.

If you're unsure, start with the automatic selection (level 0) and let the preset choose. You can then experiment with explicit levels if needed.

import * as CP from "optalcp";

let model = new CP.Model();
// ... build your model with noOverlap constraints ...

// Let the preset decide (default)
let result = await model.solve();

// Or use maximum propagation for dense problems
result = await model.solve({ noOverlapPropagationLevel: 4 });

// Or use minimum propagation for very large problems
result = await model.solve({ noOverlapPropagationLevel: 1 });
See​

positionPropagationLevel?​

optional positionPropagationLevel: number

How much to propagate position expressions on noOverlap constraints

Remarks​

This parameter controls the amount of propagation done for position expressions on noOverlap constraints. The bigger the value, the more algorithms are used for propagation. It means that more time is spent by the propagation, and possibly more values are removed from domains. However, more propagation doesn't necessarily mean better performance. FDS search (see Parameters.searchType) usually benefits from higher propagation levels.

The parameter takes an integer value in range 1..3.

The default value is 2.

reservoirPropagationLevel?​

optional reservoirPropagationLevel: number

How much to propagate constraints on cumul functions

Remarks​

This parameter controls the amount of propagation done for CumulExpr.le and CumulExpr.ge when used together with steps (Model.stepAtStart, Model.stepAtEnd, Model.stepAt).

The bigger the value, the more algorithms are used for propagation. It means that more time is spent by the propagation, and possibly more values are removed from domains. More propagation doesn't necessarily mean better performance. FDS search (see Parameters.searchType) usually benefits from higher propagation levels.

The parameter takes an integer value in range 1..2.

The default value is 1.

usePrecedenceEnergy?​

optional usePrecedenceEnergy: number

Whether to use precedence energy propagation algorithm

Remarks​

Precedence energy algorithm improves propagation of precedence constraints when an interval has multiple predecessors (or successors) which use the same resource (noOverlap or cumulative constraint). In this case, the predecessors (or successors) may be in disjunction. Precedence energy algorithm can leverage this information and propagate the precedence constraint more aggressively.

The parameter takes an integer value: 0 to disable, 1 to enable.

The default value is 0.

Simple Lower Bound​

simpleLBMaxIterations?​

optional simpleLBMaxIterations: number

Maximum number of feasibility checks

Remarks​

Simple lower bound is computed by binary search for the best objective value that is not infeasible by propagation. This parameter limits the maximum number of iterations of the binary search. When the value is 0, then simple lower bound is not computed at all.

The parameter takes an integer value in range 0..2147483647.

The default value is 2147483647.

simpleLBShavingRounds?​

optional simpleLBShavingRounds: number

Number of shaving rounds

Remarks​

When non-zero, the solver shaves on variable domains to improve the lower bound. This parameter controls the number of shaving rounds.

The parameter takes an integer value in range 0..2147483647.

The default value is 0.

simpleLBWorker?​

optional simpleLBWorker: number

Which worker computes simple lower bound

Remarks​

Simple lower bound is a bound such that infeasibility of a better objective can be proved by propagation only (without the search). The given worker computes simple lower bound before it starts the normal search. If a worker with the given number doesn't exist, then the lower bound is not computed.

The parameter takes an integer value in range -1..2147483647.

The default value is 0.

Terminal output​

color?​

optional color: "Never" | "Auto" | "Always"

Whether to colorize output to the terminal

Remarks​

This parameter controls when terminal output is colorized. Possible values are:

  • Never: don't colorize the output.
  • Auto: colorize if the output is a supported terminal.
  • Always: always colorize the output.

The default value is Auto.

Trace​

infoTraceLevel?​

optional infoTraceLevel: number

Level of information trace

Remarks​

This parameter is available only in the development edition of the solver.

When set to a value bigger than zero, the solver prints various high-level information. The higher the value, the more information is printed.

The parameter takes an integer value in range 0..5.

The default value is 0.

propagationTraceLevel?​

optional propagationTraceLevel: number

Level of propagation trace

Remarks​

This parameter is available only in the development edition of the solver.

When set to a value bigger than zero, the solver prints a trace of the propagation, that is a line for every domain change. The higher the value, the more information is printed.

The parameter takes an integer value in range 0..5.

The default value is 0.

searchTraceLevel?​

optional searchTraceLevel: number

Level of search trace

Remarks​

This parameter is available only in the development edition of the solver.

When set to a value bigger than zero, the solver prints a trace of the search. The trace contains information about every choice taken by the solver. The higher the value, the more information is printed.

The parameter takes an integer value in range 0..5.

The default value is 0.

Remarks​

Parameters specify how the solver should behave. For example, the number of workers (threads) to use, the time limit, etc.

Parameters can be passed to the solver functions Model.solve and Solver.solve.

Examples​

In the following example, we are using the TimeLimit parameter to specify that the solver should stop after 5 minutes. We also specify that the solver should use 4 threads. Finally, we specify that the solver should use FDS search (in all threads).

let params = {
  timeLimit: 300, // In seconds, i.e. 5 minutes
  nbWorkers: 4,   // Use 4 threads
  searchType: "FDS"
};
let result = await myModel.solve(params);

Worker-specific parameters​

Some parameters can be specified differently for each worker. For example, some workers may use LNS search while others use FDS search. To specify worker-specific parameters, use the workers parameter and pass an array of WorkerParameters.

Not all parameters can be specified per worker. For example, TimeLimit is a global parameter. See WorkerParameters for the list of parameters that can be specified per worker.

If a parameter is not set specifically for a worker, the global value is used.

In the following example, we are going to use 4 workers; two of them will run FDS search and the remaining two will run LNS search. In addition, workers that use FDS search will use increased propagation levels.

// Parameters for a worker that uses FDS search.
// FDS works best with increased propagation levels, so set them:
let fdsWorker: CP.WorkerParameters = {
  searchType: "FDS",
  noOverlapPropagationLevel: 4,
  cumulPropagationLevel: 3,
  reservoirPropagationLevel: 2
};
// Global parameters:
let params = {
  timeLimit: 60,       // In seconds, i.e. 1 minute
  searchType: "LNS",   // The default search type. It is not necessary, as "LNS" is the default value.
  nbWorkers: 4,        // Use 4 threads
  // The first two workers will use FDS search.
  // The remaining two workers will use the defaults, i.e., LNS search with default propagation levels.
  workers = [fdsWorker, fdsWorker];
};
let result = await myModel.solve(params);

See​

WorkerParameters for worker-specific parameters.