A Benchmark Study on Steepest Descent and Conjugate Gradient Methods-Line Search Conditions Combinations in Unconstrained Optimization
In this paper, it is aimed to computationally conduct a performance benchmarking for the steepest descent and the three well-known conjugate gradient methods (i.e., Fletcher-Reeves, Polak- Ribiere and Hestenes-Stiefel) along with six different step length calculation techniques/conditions, namely B...
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| Format: | Article |
| Language: | English |
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Croatian Operational Research Society
2022-01-01
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| Series: | Croatian Operational Research Review |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/406247 |
| _version_ | 1811223892769898496 |
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| author | Kadir Kiran |
| author_facet | Kadir Kiran |
| author_sort | Kadir Kiran |
| collection | DOAJ |
| description | In this paper, it is aimed to computationally conduct a performance benchmarking for the steepest descent and the three well-known conjugate gradient methods (i.e., Fletcher-Reeves, Polak-
Ribiere and Hestenes-Stiefel) along with six different step length calculation techniques/conditions, namely Backtracking, Armijo-Backtracking, Goldstein, weakWolfe, strongWolfe, Exact local minimizer
in the unconstrained optimization. To this end, a series of computational experiments on a test function set is completed using the combinations of those optimization methods and line search conditions.
During these experiments, the number of function evaluations for every iteration are monitored and recorded for all the optimization method-line search condition combinations. The total number of
function evaluations are then set a performance measure when the combination in question converges to the functions minimums within the given convergence tolerance. Through those data, the performance and data profiles are created for all the optimization method-line search condition combinations with the purpose of a reliable and an efficient benchmarking. It has been determined that, for this test function set, the steepest descent-Goldstein combination is the fastest one whereas the steepest descent-exact local minimizer is the most robust one with a high convergence accuracy. By making a trade-off between convergence speed and robustness, it has been identified that the steepest descent-weak Wolfe combination is the optimal choice for this test function set. |
| first_indexed | 2024-04-12T08:40:19Z |
| format | Article |
| id | doaj.art-12537bfad33449949ed84a2d41ee1f0a |
| institution | Directory Open Access Journal |
| issn | 1848-0225 1848-9931 |
| language | English |
| last_indexed | 2024-04-12T08:40:19Z |
| publishDate | 2022-01-01 |
| publisher | Croatian Operational Research Society |
| record_format | Article |
| series | Croatian Operational Research Review |
| spelling | doaj.art-12537bfad33449949ed84a2d41ee1f0a2022-12-22T03:39:53ZengCroatian Operational Research SocietyCroatian Operational Research Review1848-02251848-99312022-01-011317797A Benchmark Study on Steepest Descent and Conjugate Gradient Methods-Line Search Conditions Combinations in Unconstrained OptimizationKadir Kiran0Department of Airframe and Powerplant MaintenanceIn this paper, it is aimed to computationally conduct a performance benchmarking for the steepest descent and the three well-known conjugate gradient methods (i.e., Fletcher-Reeves, Polak- Ribiere and Hestenes-Stiefel) along with six different step length calculation techniques/conditions, namely Backtracking, Armijo-Backtracking, Goldstein, weakWolfe, strongWolfe, Exact local minimizer in the unconstrained optimization. To this end, a series of computational experiments on a test function set is completed using the combinations of those optimization methods and line search conditions. During these experiments, the number of function evaluations for every iteration are monitored and recorded for all the optimization method-line search condition combinations. The total number of function evaluations are then set a performance measure when the combination in question converges to the functions minimums within the given convergence tolerance. Through those data, the performance and data profiles are created for all the optimization method-line search condition combinations with the purpose of a reliable and an efficient benchmarking. It has been determined that, for this test function set, the steepest descent-Goldstein combination is the fastest one whereas the steepest descent-exact local minimizer is the most robust one with a high convergence accuracy. By making a trade-off between convergence speed and robustness, it has been identified that the steepest descent-weak Wolfe combination is the optimal choice for this test function set.https://hrcak.srce.hr/file/406247conjugate gradientline searchstep lengthsteepest descentoptimization |
| spellingShingle | Kadir Kiran A Benchmark Study on Steepest Descent and Conjugate Gradient Methods-Line Search Conditions Combinations in Unconstrained Optimization Croatian Operational Research Review conjugate gradient line search step length steepest descent optimization |
| title | A Benchmark Study on Steepest Descent and Conjugate Gradient Methods-Line Search Conditions Combinations in Unconstrained Optimization |
| title_full | A Benchmark Study on Steepest Descent and Conjugate Gradient Methods-Line Search Conditions Combinations in Unconstrained Optimization |
| title_fullStr | A Benchmark Study on Steepest Descent and Conjugate Gradient Methods-Line Search Conditions Combinations in Unconstrained Optimization |
| title_full_unstemmed | A Benchmark Study on Steepest Descent and Conjugate Gradient Methods-Line Search Conditions Combinations in Unconstrained Optimization |
| title_short | A Benchmark Study on Steepest Descent and Conjugate Gradient Methods-Line Search Conditions Combinations in Unconstrained Optimization |
| title_sort | benchmark study on steepest descent and conjugate gradient methods line search conditions combinations in unconstrained optimization |
| topic | conjugate gradient line search step length steepest descent optimization |
| url | https://hrcak.srce.hr/file/406247 |
| work_keys_str_mv | AT kadirkiran abenchmarkstudyonsteepestdescentandconjugategradientmethodslinesearchconditionscombinationsinunconstrainedoptimization AT kadirkiran benchmarkstudyonsteepestdescentandconjugategradientmethodslinesearchconditionscombinationsinunconstrainedoptimization |