RASON Analytics API Help
Download RASON User Guide
Download RASON Reference
Introduction to RASON
About RASON Models and the RASON Server
Rason Subscriptions
Rason Web IDE
Creating and Running a Decision Flow
Defining Your Optimization Model
Defining Your Simulation Model
Performing Sensitivity Analysis
Defining Your Stochastic Optimization Model
Defining Your Data Science Model
Defining Custom Types
Defining Custom Functions
Defining Your Decision Table
Defining Contexts
Using the REST API
REST API Quick Call Endpoints
REST API Endpoints
Decision Flow REST API Endpoints
OData Endpoints
OData Service for Decision Flows
Creating Your Own Application
Using Arrays, For, Loops and Tables
Organization Accounts

REST Endpoint: GET: https://rason.net/api/model/{nameorid}/result

A REST API endpoint that retrieves the results from the model previously submitted to GET/POST rason.net/api/model/{nameorid}/optimize, GET/POST rason.net/api/model/{nameorid}/simulate, GET/POST rason.net/api/model/{nameorid}/decision, GET/POST rason.net/api/model/{nameorid}/datamine, GET/POST rason.net/api/model/{nameorid}/diagnose or GET/POST rason.net/api/model/{nameorid}/solve.

Note: Use GET rason.net/api/model/{nameorid}/result/fitted-{name} to retrieve the text of the fittedModel.

Note: If solving a hard mixed integer model or nonsmooth model, calling GET rason.net/api/model/id/result will return the best solution found so far and the number of subproblems completed.

  • URL:https://rason.net/api/model/{nameorid}/result
  • Method: GET
  • URL Params

    Required: None

    Optional: None

  • Headers:

    Required: Authorization - Example: Authorization: bearer {your RASON token}

    Optional: None

  • Data Params: None
  • Success Response

    Code: 200 (OK)

    Response Example: Results from a previously solved optimization model. 

            
      {
          "status": {
            "code": 0,
            "id": "2590+2019-11-19-17-56-14-563925",
            "codeText": "Solver found a solution.  All constraints and optimality conditions are satisfied."
          },
          "variables": {
            "d9:f9": {
              "finalValue": [
                [200,200,0 ]
              ]
            }
          },
          "objective": {
            "d18": {
              "finalValue": 25000
            }
          }
      }

  • Example Response: Results from a previously solved decision flow using POST rason.net/api/model/{nameorid}/solve.

    Notes:

    1. Overall flow “status” contains the id of the actual model instance that was created after calling POST rason.net/api/model/{nameorid}/solve.
    2. Overall flow "status" shows "solveTimestamp" indicating the moment in time when the entire flow was solved.
    3. The “status” object for a decision flow stage contains the id that corresponds to a model on the server:
      1. For reusable stages, the id refers to origin/version resource id of the rason/excel reusable model
      2. For inline stages, the id refers to the resource id of the enclosing flow model instance.
    4. The "status" object for a decision flow stage displays "solveTimestamp" indicating the moment in time when the stage was solved.
          
{
    "status": {
        "id": "2590+mlrDF+2020-12-23-00-44-21-465964",
        "code": 0,
        "codeText": "Success",
        "solveTimestamp": "2020-12-23-00-44-31-738690",
        "solveTime": 3333
    },
    "results": {
        "mlr-reusable.myModel.anova": [],
        "mlr-reusable.myModel.coefficients": [],
        "mlr-reusable.myModel.detailedCoefficients": [],
        "mlr-reusable.myModel.detailedResiduals": [],
        "mlr-reusable.myModel.entranceTolerance": [],
        "mlr-reusable.myModel.influenceDiagnostics": [],
        "mlr-reusable.myModel.multicollinearityDiagnostics": [],
        "mlr-reusable.myModel.predictorScreeningInfo": [],
        "mlr-reusable.myModel.regressionSummary": [],
        "mlr-reusable.myModel.varianceCovariance": [],
        "mlr-reusable.newScore.prediction": [],
        "mlr-reusable.testScore.intervals": [],
        "mlr-reusable.testScore.mad": [],
        "mlr-reusable.testScore.mse": [],
        "mlr-reusable.testScore.prediction": [],
        "mlr-reusable.testScore.r2": [],
        "mlr-reusable.testScore.residuals": [],
        "mlr-reusable.testScore.rmse": [],
        "mlr-reusable.testScore.ss": [],
        "mlr-reusable.testScore.sse": [],
        "mlr-reusable.testScore.sst": [],
        "mlr-reusable.trainScore.intervals": [],
        "mlr-reusable.trainScore.mad": [],
        "mlr-reusable.trainScore.mse": [],
        "mlr-reusable.trainScore.prediction": [],
        "mlr-reusable.trainScore.r2": [],
        "mlr-reusable.trainScore.residuals": [],
        "mlr-reusable.trainScore.rmse": [],
        "mlr-reusable.trainScore.ss": [],
        "mlr-reusable.trainScore.sse": [],
        "mlr-reusable.trainScore.sst": [],
        "mlr-reusable.validScore.intervals": [],
        "mlr-reusable.validScore.mad": [],
        "mlr-reusable.validScore.mse": [],
        "mlr-reusable.validScore.prediction": [],
        "mlr-reusable.validScore.r2": [],
        "mlr-reusable.validScore.residuals": [],
        "mlr-reusable.validScore.rmse": [],
        "mlr-reusable.validScore.ss": [],
        "mlr-reusable.validScore.sse": [],
        "mlr-reusable.validScore.sst": []
    },
    "mlr-reusable": {
        "status": {
            "id": "2590+mlr-reusable+2020-12-22-17-17-28-531289",
            "code": 0,
            "codeText": "Success",
            "solveTimestamp": "2020-12-23-00-44-31-726603",
            "solveTime": 807
        },
        "myModel": {
            "anova": {
                "objectType": "dataFrame",
                "name": "ANOVA",
                "order": "col",
                "rowNames": ["Regression", "Error", "Total"],
                "colNames": ["DF", "SS", "MS", "F-Statistic", "P-Value"],
                "colTypes": ["double", "double", "double", "double", "double"],
                "indexCols": null,
                "data": [
                    [5, 1, 6],
                    [1481.7379525361716, 21.239190320971829, 1502.9771428571435],
                    [296.34759050723432, 21.239190320971829, 250.49619047619058],
                    [13.9528666596399, null, null],
                    [0.200386619063231, null, null]
                ]
            },
            "influenceDiagnostics": {
                "objectType": "dataFrame",
                "name": "Influence Diagnostics",
                "order": "col",
                "rowNames": ["Record 1", "Record 2", "Record 3", "Record 4", "Record 5",      
                "Record 6", "Record 7"],
                "colNames": ["Cook's Distance", "DFFITS", "Covariance Ratio", "Leverage", 
                "Delete-1 Variance"],
                "colTypes": ["double", "double", "double", "double", "double"],
                "indexCols": null,
                "data": [
                    [107.47518518527227, 51.424990138083416, 0.14188702913422824, 
                     1.0631545936513054, 2.913987270808915, 0.61818025245238661,  
                     2.9727210098095447],
                    [null, null, null, null, null, null, null],
                    [null, null, null, null, null, null, null],
                    [0.99845165552432136, 0.99676950350136173, 0.45984550198283475, 
                     0.86447895149935328, 0.94589893248338786, 0.787644364006993, 
                     0.94691109100174853],
                    [null, null, null, null, null, null, null]
                ]
            },
            "detailedResiduals": {
                "objectType": "dataFrame",
                "name": "Residuals",
                "order": "col",
                "rowNames": ["Record 1", "Record 2", "Record 3", "Record 4", "Record 5", 
                 "Record 6", "Record 7"],
                "colNames": ["Raw", "Standardized", "Studentized", "Deleted"],
                "colTypes": ["double", "double", "double", "double"],
                "indexCols": null,
                "data": [
                    [0.18134382537429872, 0.261941081096154, 3.3870996717127468, -
                     1.6965722329459254, -1.071943501101245, -2.1237376882720014, 
                     1.0618688441359865],
                    [0.039349008573019859, 0.056837456827683354, 0.73495203790261954, -
                     0.36813183576085584, -0.23259636178714821, -0.46082061151062514, 
                     0.23041030575530949],
                    [1.0000000000003062, 1.0000000000000773, 1.0000000000000446, -
                     1.0000000000000406, -1.0000000000000535, -1.0000000000000457, 
                     1.0000000000000351],
                    [null, null, null, null, null, null, null]
                ]
            },
            "coefficients": {
                "objectType": "dataFrame",
                "name": "Coefficients",
                "order": "col",
                "rowNames": ["Intercept", "X1", "X2", "X3", "X4", "Weights"],
                "colNames": ["Estimate"],
                "colTypes": ["double"],
                "indexCols": null,
                "data": [
                    [-23.487082869309749, 149.01166572428048, 415.960477398573, 
                     121.07640892657103, 151.68899484535496, -7.9279668096112808]
                ]
            },
            "regressionSummary": {
                "objectType": "dataFrame",
                "name": "Regression Summary",
                "order": "col",
                "rowNames": ["Residual DF", "R2", "Adjusted R2", "Std. Error Estimate", 
                "RSS"],
                "colNames": ["Value"],
                "colTypes": ["double"],
                "indexCols": null,
                "data": [
                    [1, 0.98586858727565452, 0.91521152365392722, 4.6085996051915625, 
                     21.239190320971829]
                ]
            },
            "detailedCoefficients": {
                "objectType": "dataFrame",
                "name": "Coefficients",
                "order": "col",
                "rowNames": ["Intercept", "X1", "X2", "X3", "X4", "Weights"],
                "colNames": ["Estimate", "Confidence Interval: Lower", "Confidence 
                 Interval: Upper", "Standard Error", "T-Statistic", "P-Value"],
                "colTypes": ["double", "double", "double", "double", "double", "double"],
                "indexCols": null,
                "data": [
                    [-23.487082869309749, 149.01166572428048, 415.960477398573, 
                     121.07640892657103, 151.68899484535496, -7.9279668096112808],
                    [-9817.2912678486209, -6420.4203245593817, -30105.25625420154, -
                     9828.3505418376717, -21462.07830169308, -441.60206686215588],
                    [9770.3171021100025, 6718.443656007943, 30937.177208998688, 
                     10070.503359690812, 21765.456291383787, 425.7461332429333],
                    [770.78910566396416, 517.02551050357522, 2402.071851141035, 
                     783.03688295200607, 1701.0403771476965, 34.130891879765663],
                    [-0.030471477472528336, 0.28820950358744446, 0.17316737515615238, 
                      0.15462414550655598, 0.089174247056796482, -0.23228126699822157],
                    [0.980607255587423, 0.82136128652147955, 0.89084075162002208, 
                     0.90233664276260372, 0.94337967667800116, 0.85470175050792052]
                ]
            },
            "multicollinearityDiagnostics": {
                "objectType": "dataFrame",
                "name": "Multicollinearity Diagnostics",
                "order": "col",
                "rowNames": ["Eigenvalue", "Condition Number", "Intercept", "X1", "X2", 
                 "X3", "X4", "Weights"],
                "colNames": ["Component 1", "Component 2", "Component 3", "Component 4", 
                 "Component 5", "Component 6"],
                "colTypes": ["double", "double", "double", "double", "double", "double"],
                "indexCols": null,
                "data": [
                    [3.6730071738696753E-06, 1180.0164678487108, 0.99997945561313217, 
                    0.990267656034644, 0.9991944090799727, 0.99224894926150142, 0.999758767919465, 
                    0.00084224958727100536],
                    [0.010435918746027308, 22.137756794176649, 1.7240620011455569E-05, 
                    0.0088257997391910665, 0.00078674087194367207, 0.00731719974698259, 
                    6.5640162959100873E-05, 0.12834187943550959],
                    [0.077015742133040577, 8.1490916865528664, 3.0578560755202531E-06, 
                    0.00017935197320540931, 6.5721043606337055E-07, 0.00011897926922988472, 
                    0.00012275090771918826, 0.83130061038308478],
                    [0.27182022378006571, 4.3376856294185409, 2.2986685440246014E-09, 
                    0.00046250118143391085, 8.0394468246562482E-06, 0.000300234112295231, 
                    3.2232605484255242E-05, 3.7656279853642748E-05],
                    [0.52628650423904266, 3.1173663258103557, 4.9668359866317463E-08, 
                    0.000252242229389297, 9.4036357441602486E-06, 7.5579438000784511E-06, 
                    1.9453368308914693E-05, 0.03617747936598411],
                    [5.1144379380946488, 1, 1.9394375241198394E-07, 1.2448842136433821E-05, 
                    7.4975507874111751E-07, 7.0796661908484751E-06, 1.1550360633061476E-06, 
                    0.0033001249482969112]
                ]
            },
            "varianceCovariance": {
                "objectType": "dataFrame",
                "name": "Variance-Covariance Matrix of Coefficients",
                "order": "col",
                "rowNames": ["Intercept", "X1", "X2", "X3", "X4", "Weights"],
                "colNames": ["Intercept", "X1", "X2", "X3", "X4", "Weights"],
                "colTypes": ["double", "double", "double", "double", "double", "double"],
                "indexCols": null,
                "data": [
                    [594115.84541025374, -396732.72786361113, -1850505.4001221072, 
                    -601408.49507572281, -1310903.2853242843,  760.22970426963764],
                    [-396732.72786361113, 267315.37851148256, 1232078.7696380834, 
                    404340.33514438733, 874334.74061642145, -940.12886509650309],
                    [-1850505.4001221072, 1232078.7696380834, 5769949.1780441189, 
                    1868436.9332559798, 4084730.3785933196, -1657.4586767497408],
                    [-601408.49507572281, 404340.33514438733, 1868436.9332559798, 
                    613146.76006319362, 1325771.9443042604, -1836.5072377616627],
                    [-1310903.2853242843, 874334.74061642145, 4084730.3785933196, 
                    1325771.9443042604, 2893538.3646867773, -2052.4309307878093],
                    [760.22970426963764, -940.12886509650309, -1657.4586767497408, 
                    -1836.5072377616627, -2052.4309307878093, 1164.9177805082536]
                ]
            },
            "predictorScreeningInfo": {
                "objectType": "dataFrame",
                "name": "Predictor Screening",
                "order": "col",
                "rowNames": ["Intercept", "X1", "X2", "X3", "X4", "Weights"],
                "colNames": ["Criteria", "Included"],
                "colTypes": ["double", "wstring"],
                "indexCols": null,
                "data": [
                    [2.6457513110645907, 0.18910238671962837, 0.0019185935687154323, 
                     0.051188877004303704, 0.21616064003500096, 0.14905171986625759],
                    ["TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"]
                ]
            },
            "entranceTolerance": 4.1122383477614725E-15
        },
        "trainScore": {
            "prediction": {
                "objectType": "dataFrame",
                "name": "Scores: trainData",
                "order": "col",
                "rowNames": ["Record 1", "Record 2", "Record 3", "Record 4", "Record 5", 
                 "Record 6", "Record 7"],
                "colNames": ["Prediction: Y"],
                "colTypes": ["double"],
                "indexCols": null,
                "data": [
                    [78.3186561746257, 113.03805891890384, 105.81290032828726, 
                     111.09657223294593, 94.171943501101239, 89.723737688272, 
                     73.238131155864011]
                ]
            },
            "residuals": {
                "objectType": "dataFrame",
                "name": "Residuals: trainData",
                "order": "col",
                "rowNames": ["Record 1", "Record 2", "Record 3", "Record 4", "Record 5", 
                 "Record 6", "Record 7"],
                "colNames": ["Value"],
                "colTypes": ["double"],
                "indexCols": null,
                "data": [
                    [0.18134382537429872, 0.261941081096154, 3.3870996717127468, -
                     1.6965722329459254, -1.071943501101245, -2.1237376882720014, 
                     1.0618688441359865]
                ]
            },
            "sse": 21.239190320973723,
            "ss": 64754,
            "sst": 1502.9771428571432,
            "mse": 3.0341700458533891,
            "rmse": 1.7418869210868395,
            "mad": 1.3977866920911939,
            "r2": 0.9858685872756533
        },
        "validScore": {
            "prediction": {
                "objectType": "dataFrame",
                "name": "Scores: validData",
                "order": "col",
                "rowNames": ["Record 1", "Record 2", "Record 3", "Record 4"],
                "colNames": ["Prediction: Y"],
                "colTypes": ["double"],
                "indexCols": null,
                "data": [
                    [88.386069640329225, 103.28937334867146, 118.19379758053017, 
                     106.12656662832126]
                ]
            },
            "residuals": {
                "objectType": "dataFrame",
                "name": "Residuals: validData",
                "order": "col",
                "rowNames": ["Record 1", "Record 2", "Record 3", "Record 4"],
                "colNames": ["Value"],
                "colTypes": ["double"],
                "indexCols": null,
                "data": [
                    [-4.586069640329228, 1.0106266513285362, -2.2937975805301676, -3.4265666283212539]
                ]
            },
            "sse": 39.056267173095925,
            "ss": 41881.03,
            "sst": 529.80750000000023,
            "mse": 9.7640667932739813,
            "rmse": 3.1247506769779219,
            "mad": 2.8292651251272964,
            "r2": 0.92628215498441246
        },
        "testScore": {
            "prediction": {
                "objectType": "dataFrame",
                "name": "Scores: testData",
                "order": "col",
                "rowNames": ["Record 1", "Record 2"],
                "colNames": ["Prediction: Y"],
                "colTypes": ["double"],
                "indexCols": null,
                "data": [
                    [93.148609395507151, 82.823761907438126]
                ]
            },
            "residuals": {
                "objectType": "dataFrame",
                "name": "Residuals: testData",
                "order": "col",
                "rowNames": ["Record 1", "Record 2"],
                "colNames": ["Value"],
                "colTypes": ["double"],
                "indexCols": null,
                "data": [
                    [2.7513906044928547, -10.323761907438126]
                ]
            },
            "sse": 114.15021017996204,
            "ss": 14453.060000000001,
            "sst": 273.78000000000014,
            "mse": 57.075105089981022,
            "rmse": 7.5548067539799471,
            "mad": 6.53757625596549,
            "r2": 0.58305862305514644
        },
        "newScore": {
            "prediction": {
                "objectType": "dataFrame",
                "name": "Scores: newData",
                "order": "col",
                "rowNames": ["Record 1", "Record 2", "Record 3", "Record 4", 
                "Record 5", "Record 6", "Record 7", "Record 8", "Record 9", 
                "Record 10", "Record 11", "Record 12", "Record 13"],
                "colNames": ["Prediction: Y"],
                "colTypes": ["double"],
                "indexCols": null,
                "data": [
                    [79.111452855586833, 75.616521198747392, 104.87496671059371, 
                    91.309331050194245, 93.941406076468283, 106.60569700924839, 
                    106.91936330928239, 84.409355269360375, 94.964740182062371, 
                    118.98659426149131, 90.7644596832126, 113.83085559986498, 
                    111.88936891390706]
                ]
            }
        }
    }
}

The chart below contains the available "code" and "codetext" responses for RASON for optimization and simulation. For a list of Rason error codes, see the previous RASON Error Codes topic.

Code CodeText Explanation
"The remote server returned an error: (401) Unauthorized." Returned if validation token is invalid.
0 Solver found a solution. All constraints and optimality conditions are satisfied. This means that the Solver has found the optimal or “best” solution under the circumstances. The exact meaning depends on whether you are solving a linear or quadratic, smooth nonlinear, global optimization, or integer programming problem, as outlined in the RASON Reference Guide.
1 Solver has converged to the current solution. All constraints are satisfied. This means that Solver has found a series of “best” solutions that satisfy the constraints, and that have very similar objective function values.
2 Solver cannot improve the current solution. All constraints are satisfied. This means that the Solver has found solutions that satisfy the constraints, but it has been unable to further improve the objective, even though the tests for optimality and convergence have not yet been satisfied.
3 Stop chosen when the maximum iteration limit was reached. This result is returned when the Solver has completed the maximum number of iterations, or trial solutions, as specified for "iterations" in "engineSettings". The default setting for this option is unlimited.
4 The objective (Set Cell) values do not converge. This result is returned when the Solver is able to increase (if you are trying to Maximize) or decrease (for Minimize) without limit the value calculated by the objective, while still satisfying the constraints.
5 Solver could not find a feasible solution. This result is returned when the Solver could not find any combination of values for the decision variables that allows all of the constraints to be satisfied simultaneously.
6 Solver stopped at user’s request. This result is returned only when the REST API endpoint, POST RASON.net/api/model/id/stop is called.
7 The linearity conditions required by this Solver engine are not satisfied. This result is returned if you’ve selected the LP/Quadratic Solver and the Solver’s tests determine that the constraints are not linear functions of the variables or the objective is not a linear or convex quadratic function of the variables; or if you’ve selected the SOCP Barrier Solver and the Solver’s tests determine that the constraints or the objective are not linear or convex quadratic functions of the variables.
8 {"Exception": "You have _____ variables. Your license allows _____ variables."} This result is returned when the Solver determines that your model is too large for the selected Solver engine within your Account tier.
9 Solver encountered an error value in a target or constraint cell. This message appears when the Solver SDK Platform (on the RASON server or on your desktop) evaluates the formulas in your RASON model and discovers an error value while calculating the objective function, uncertain function or one of your constraints.
10 Stop chosen when the maximum time limit was reached. This result is returned when Solver has run for the maximum time (number of seconds) specified for maxTime within engineSettings. The default setting for this option is unlimited.
11 There is not enough memory available to solve the problem. This message appears when the Solver could not allocate the memory it needs to solve the problem.
12 No model inputs defined. This message means that the internal “model” (information about the variable cells, objective, constraints, Solver options, etc.) is not in a valid form.
14 Solver found an integer solution within tolerance. All constraints are satisfied. If you are solving a mixed-integer programming problem (any problem with integer constraints) with a non-zero value for the intTolerance within engineSettings, the Branch & Bound method has found a solution satisfying the constraints (including the integer constraints) where the relative difference of this solution’s objective value from the true optimal objective value does not exceed the integer Tolerance setting.
15 Stop chosen when the maximum number of feasible [integer] solutions was reached. If you are using the Evolutionary Solver, this result is returned when the Solver has found the maximum number of feasible solutions (values for the variables that satisfy all constraints) allowed by the maxFeasibleSols option setting within engineSettings.
16 Stop chosen when the max number of feasible [integer] subproblems was reached. If you are using the Evolutionary Solver, this result is returned when the Solver has explored the maximum number of subproblems specified for maxSubproblems within engineSettings. You may increase the value for the maxSubproblems, leave the option setting at its default, unlimited.
17 Solver converged in probability to a global solution. If you are using the multistart methods for global optimization, with the standard LSGRG solver, or a field-installable nonlinear Solver engine, this result is returned when the multistart method’s Bayesian test has determined that all of the locally optimal solutions have probably been found; the solution displayed on the worksheet is the best of these locally optimal solutions, and is probably the globally optimal solution to the problem.
18 All variables must have both upper and lower bounds. If you are using the Interval Global Solver, this message is returned if you have not defined lower and upper bounds on all of the decision variables in the problem.
19 Variable bounds conflict in binary or alldifferent constraint. This result is returned if you have both a binary or alldifferent constraint on a decision variable and a <= or >= constraint on the same variable (that is inconsistent with the binary or alldifferent specification), or if two or more of the same decision variables appear in more than one alldifferent constraint.
20 Lower and upper bounds on variables allow no feasible solution. This result is returned if you’ve defined lower and upper bounds on a decision variable, where the lower bound is greater than the upper bound.
21 Solver encountered an error computing derivatives. This message appears when the Interpreter in Solver SDK Platform encounters an error when computing derivatives via automatic differentiation.
22 Variable appears in more than one cone constraint. This result is returned if the same decision variable appears in more than one cone constraint.
23 Formula depends on uncertainties, must be summarized or transformed. This result is returned if you've defined constraints or an objective computed by formulas that depend on uncertain parameters.
25 Simulation optimization doesn't handle models with recourse decisions. This result is returned if you’ve defined a recourse decision variable, but you’ve set "simulationOptimization": True within "modelSettings".
26 Solver could not find a feasible solution to the robust chance constrained problem. This message means that the Solver could not find a feasible solution to the robust counterpart problem. It does not necessarily mean that there is no feasible solution to the original problem; the robust counterpart is an approximation to the problem defined by your chance constraints that may yield conservative solutions which over-satisfy the chance constraints.
27 Solver found a conservative solution to the robust chance constrained problem. All constraints are satisfied. The message means that the Solver found an optimal solution to the robust counter¬part model, but when this solution was tested against your original model (using Monte Carlo simulation to test satisfaction of the chance constraints), the solution over-satisfied the chance constraints; this normally means that the solution is ‘conservative’ and the objective function value can be further improved. An alternative course of action is to manually adjust the Chance measures of selected chance constraints, by setting the option, chanceAutoAdjust: True in modelSettings, and re-solve the problem. The automatic improvement algorithm uses general-purpose methods to find an improved solution; you may be able to do better by adjusting Chance measures based on your knowledge of the problem.
28 Solver has converged to the current solution of the robust chance constrained problem. All constraints are satisfied. This result may be returned when you solve a model with uncertainty and chance constraints using robust optimization, and you’ve set "chanceAutoAdjust": True within "modelSettings".
999 Unexpected error. Please contact Technical Support. This status signifies that an unexpected exception has occurred within Solver.
1000 Interval Solver requires strictly smooth functions. The Interval Global Solver considers the ‘special’ functions ABS, IF, MAX, MIN or SIGN nonsmooth.
1001 Function cannot be evaluated for given real or interval arguments. This message may appear (instead of “Solver encountered an error value…”) if the Interval Global Solver encounters an arithmetic operation or function that it cannot evaluate for the current values of the decision variables.
1002 Solution found, but not proven globally optimal./td> This message indicates that the Interval Global Solver has systematically explored the solution space and has found a solution that is very probably the global optimum, but it has not been able to “prove global optimality.”

The chart below contains the available "code" and "codetext" responses for RASON DM.

Code CodeText Explanation
0 "Success" Data science method was completed successfully.
-1 "Execution Error" Indicates an error has occurred during the data science process.
-2 "Terminated by User Request" Indicates that solving process was terminated by the User.
-3 "Error with Algorithm Parameters" An erroneous parameter has been submitted to a data science algorithm.
-4 "Rason Inpterpreter Error" Indicates there is an error with Rason model syntax/data types/keywords, etc.
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