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Passed
run_tests.TestSimulation_mosek_conif.test_simulate (from run_tests.TestSimulation_mosek_conif-20200304035102)
Standard Output
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.11% on GP solve 5. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.016% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.12% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.035% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.22% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9.4% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.33% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.0029% on GP solve 6. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.027% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.024% on GP solve 6. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.065% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.076% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9.4% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.33% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.