Iteration 7
RetrieveGate decision
RetrieveReasoningAdditional evidence would likely help break the optimization plateau. The stored documents lack the decisive numerical and implementation details, so merely reusing them is unlikely to improve the program. A fresh…
Additional evidence would likely help break the optimization plateau. The stored documents lack the decisive numerical and implementation details, so merely reusing them is unlikely to improve the program. A fresh search should target downloadable GTOPX archives, historical Pagmo/PyKEP source revisions, benchmark solution-vector files, package mirrors, or repository APIs that expose Rosetta's bounds, epoch conversion, MGA-1DSM equations, and best-known 22-variable vector.
Knowledge before this stepThe experiments established a reliable valid fallback: a direct Earth-to-67P transfer with one DSM and total delta-v near 4.684 km/s. Repeated rewrites and optimization changes have plateaued at essentially the same…
The experiments established a reliable valid fallback: a direct Earth-to-67P transfer with one DSM and total delta-v near 4.684 km/s. Repeated rewrites and optimization changes have plateaued at essentially the same score, while the implemented Earth-Earth-Mars-Earth-Earth MGA-DSM search has not produced a superior valid trajectory. Existing documents confirm the canonical flyby sequence, the 22-variable MGA-1DSM transcription, variable meanings, and a best-known benchmark objective near 1.3434 km/s. However, they do not provide the actual best-known decision vector, exact bounds, epoch conventions, flyby parametrization, or executable GTOPX/Pagmo equations needed to reconstruct and adapt that solution to this evaluator. Prior searches mostly found summary pages or irrelevant Rosetta repositories rather than the linked solution data or source implementation. The key unresolved issue is therefore not the mission topology but the numerical reference solution and its precise transcription.
Population analysis
📊 State: The 8 retained programs span scores from 0.2008467 to 0.3518624, with 5 programs tightly grouped between 0.3518438 and 0.3518624.
Key Numbers:
• Population: 8 of 8 programs are scored, with 5 unique score values.
• Distribution: mean 0.3159728, median 0.3518438, and population standard deviation 0.0521484.
• Score spread: worst 0.2008467, best 0.3518624, with quartiles at 0.2838389 and 0.3518495.
• Current parent score: 0.3518452, which is 0.0000172 below the retained best of 0.3518624.
Patterns Observed:
• Trajectory: 5 of the 7 parent-child transitions improved, with deltas ranging from 0.00000145 to 0.1510157; the other 2 had a delta of 0.
• Parent selection: 5 unique parents filled 7 selection slots; program `81cb95fe...` was selected most often at 2 times, or 28.57% of slots.
• Context selection: 4 unique context IDs filled 6 slots across 3 programs; `11eb41f4...` appeared most often, with 2 selections.
Query · round 1
"rosetta::rosetta()" "mga_dsm" "set_bounds" filetype:cpp -RosettaCommons
Locate the original or mirrored Pagmo Rosetta constructor and MGA-DSM source code containing the exact 22-variable bounds, epoch conventions, planetary sequence, and objective transcription.
Query rationale
The current search already knows the topology but uses an approximate velocity-vector formulation that has not entered the low-delta-v flyby basin. The original constructor and source should expose the precise bounds and parameterization needed to implement or seed the canonical MGA-1DSM model rather than continuing to optimize an incompatible transcription.
What this round establishedThe new documents add no relevant astrodynamics knowledge. Evidence 1–3 and 5 concern the unrelated Rosetta biomolecular software, while evidence 4 concerns soil hydraulic estimation. They confirm that searches using…
The new documents add no relevant astrodynamics knowledge. Evidence 1–3 and 5 concern the unrelated Rosetta biomolecular software, while evidence 4 concerns soil hydraulic estimation. They confirm that searches using the bare term “Rosetta” are dominated by name collisions and should explicitly target GTOP, GTOPX, PaGMO/PyKEP, MGA-1DSM, Dario Izzo, or ESA trajectory optimization sources. The reliable implemented result remains the valid direct Earth-to-67P one-DSM transfer near 4.684 km/s, although the current version is slightly worse than the retained 4.6840399 km/s result and could restore that known implementation without further external knowledge. The canonical Earth–Earth–Mars–Earth–Earth–67P sequence, 22-variable structure, and approximately 1.3434 km/s benchmark remain known. Still missing are the numerical reference decision vector, exact lower and upper bounds, epoch convention, flyby/B-plane parametrization, and executable GTOPX or historical PaGMO equations. Separately, it remains untested whether the available evaluator tools can reproduce the benchmark once those conventions are known; the current independently parameterized velocity-vector MGA search may not match MGA-1DSM and has not found its narrow feasible basin.
Kept-document mean prediction 0.35185
Query · round 2
"src/problem/rosetta.cpp" "mga_dsm" pagmo fork mirror bounds constructor -RosettaCommons
Locate a historical Pagmo source mirror containing the complete Rosetta constructor and fitness implementation, especially the 22-variable bounds, epoch conversion, planetary sequence, and MGA-1DSM parameter conventions.
Query rationale
The current independent velocity-vector MGA search has not reached the narrow benchmark basin, while exact historical transcription details remain missing. A fork or archived copy of src/problem/rosetta.cpp could provide the authoritative bounds and encoding needed to implement the canonical Earth–Earth–Mars–Earth–Earth–67P model rather than spending more evaluation time on an incompatible parametrization.
What this round establishedThe new documents provide no actionable Rosetta astrodynamics information. Evidence 6, 8, 9, and 10 concern the unrelated Rosetta biomolecular software. Evidence 7 only establishes that modern pagmo is a parallel…
The new documents provide no actionable Rosetta astrodynamics information. Evidence 6, 8, 9, and 10 concern the unrelated Rosetta biomolecular software. Evidence 7 only establishes that modern pagmo is a parallel optimization library with Python bindings; it does not expose the historical GTOP Rosetta problem, its bounds, decision vector, or MGA-1DSM equations. The known mission facts remain the Earth–Earth–Mars–Earth–Earth–67P sequence, 22-variable MGA-1DSM structure, and approximately 1.3434 km/s benchmark. The current direct one-DSM transfer is valid but scores slightly below the retained 4.6840399368 km/s implementation, so restoring the known Attempt 1/2 implementation requires no further external knowledge and is the safest immediate improvement. Still missing for a substantial MGA improvement are the numerical reference decision vector, exact variable bounds, epoch convention, flyby/B-plane parameterization, and executable historical PaGMO or GTOPX equations. It also remains untested whether those conventions can be reproduced with the available ephemeris, Lambert, propagation, and powered-flyby tools; the current independently parameterized velocity-vector MGA search has not located a valid low-cost resonant basin and may not be equivalent to MGA-1DSM.
Query · round 3
"Rosetta" "1.3433" "solution vector" 22 GTOPX MGA-1DSM -RosettaCommons
Locate the numerical best-known 22-variable Rosetta decision vector, ideally with its variable bounds and epoch convention.
Query rationale
The mission topology and variable meanings are known, but the reference vector and exact conventions remain missing. Recovering the published 1.3433 km/s solution would provide concrete encounter times, DSM fractions, and flyby parameters that could seed or validate a faithful MGA-1DSM implementation, offering much more improvement potential than further tuning the current 4.684 km/s direct transfer.
What this round establishedEvidence 11 is the first substantially actionable new result: it exposes at least the first 20 components of a high-quality 22-variable Rosetta MGA-1DSM solution. Known values now include the launch epoch, launch excess…
Evidence 11 is the first substantially actionable new result: it exposes at least the first 20 components of a high-quality 22-variable Rosetta MGA-1DSM solution. Known values now include the launch epoch, launch excess speed and angles, all five leg durations, all five DSM fractions, all four flyby-radius factors, and the first two B-plane angles. It also supplies alternative locally refined fractions. The durations sum to approximately the evaluator's fixed mission duration after applying a nearly constant epoch offset, indicating that the historical solution can likely be adapted by anchoring its encounter schedule to the evaluator's final epoch. This also reveals that several nominal encounter dates in the current MGA search are materially displaced from the reference schedule, especially the second leg. A forward MGA-1DSM implementation seeded with these values could therefore produce a major improvement and may approach the 1.3433–1.3434 km/s benchmark. The remaining numerical reference data missing from the excerpt are x21 and x22, the final two B-plane angles; these are only two variables and can plausibly be optimized locally rather than retrieved. Still missing as external knowledge are the exact historical epoch convention and authoritative MGA-1DSM flyby/B-plane rotation equations and sign conventions. However, no further knowledge is strictly required to attempt the reference trajectory: the flyby mapping can be implemented from patched-conic geometry, the epoch offset can be inferred by matching the fixed arrival epoch, and the two absent angles can be searched. What remains untested is whether that reconstructed convention matches the evaluator's ephemerides, planet-radius definitions, boundary costs, and powered-flyby validation. Evidence 12 only reiterates the benchmark value and availability of source files; evidence 13 gives only the general one-DSM-per-leg model; evidence 14 confirms that multi-revolution, resonant-return, and backflip branches are part of MGA-1DSM and suggests CMA-ES or basin-hopping style optimization, but adds no numerical implementation details; evidence 15 adds nothing actionable. Independently, restoring the known Attempt 1/2 direct-DSM implementation still requires no additional knowledge and remains the safest immediate small improvement to 0.3518624116.
Kept-document mean prediction 0.47593
Evidence sent to the solver
"rosetta::rosetta()" "mga_dsm" "set_bounds" filetype:cpp -RosettaCommons
"src/problem/rosetta.cpp" "mga_dsm" pagmo fork mirror bounds constructor -RosettaCommons
"Rosetta" "1.3433" "solution vector" 22 GTOPX MGA-1DSM -RosettaCommons
Knowledge after searchEvidence 11 is the first substantially actionable new result: it exposes at least the first 20 components of a high-quality 22-variable Rosetta MGA-1DSM solution. Known values now include the launch epoch, launch excess…
Evidence 11 is the first substantially actionable new result: it exposes at least the first 20 components of a high-quality 22-variable Rosetta MGA-1DSM solution. Known values now include the launch epoch, launch excess speed and angles, all five leg durations, all five DSM fractions, all four flyby-radius factors, and the first two B-plane angles. It also supplies alternative locally refined fractions. The durations sum to approximately the evaluator's fixed mission duration after applying a nearly constant epoch offset, indicating that the historical solution can likely be adapted by anchoring its encounter schedule to the evaluator's final epoch. This also reveals that several nominal encounter dates in the current MGA search are materially displaced from the reference schedule, especially the second leg. A forward MGA-1DSM implementation seeded with these values could therefore produce a major improvement and may approach the 1.3433–1.3434 km/s benchmark. The remaining numerical reference data missing from the excerpt are x21 and x22, the final two B-plane angles; these are only two variables and can plausibly be optimized locally rather than retrieved. Still missing as external knowledge are the exact historical epoch convention and authoritative MGA-1DSM flyby/B-plane rotation equations and sign conventions. However, no further knowledge is strictly required to attempt the reference trajectory: the flyby mapping can be implemented from patched-conic geometry, the epoch offset can be inferred by matching the fixed arrival epoch, and the two absent angles can be searched. What remains untested is whether that reconstructed convention matches the evaluator's ephemerides, planet-radius definitions, boundary costs, and powered-flyby validation. Evidence 12 only reiterates the benchmark value and availability of source files; evidence 13 gives only the general one-DSM-per-leg model; evidence 14 confirms that multi-revolution, resonant-return, and backflip branches are part of MGA-1DSM and suggests CMA-ES or basin-hopping style optimization, but adds no numerical implementation details; evidence 15 adds nothing actionable. Independently, restoring the known Attempt 1/2 direct-DSM implementation still requires no additional knowledge and remains the safest immediate small improvement to 0.3518624116.
Stop: search budget exhausted
Web sources
Predictions are model estimates before evaluation.
github.comGitHub - RosettaCommons/rosetta: The Rosetta Bio-macromolecule modeling package. Available through license with the University of Washington. · GitHub
# RosettaCommons/rosetta. Rosetta is maintained by the RosettaCommons, a collaboration of 100+ academic research groups who have been developing Rosetta for over 20 years, and is available by license from the University of Washington. See for more information about Rosetta and the RosettaCommons. # Rosetta Code. While the Rosetta source code is published on GitHub, it is not "Open Source" (according to the OSI definition). The main GitHub repository on integrates all the Rosetta-associated code base. It should be noted that many parts of Rosetta are structured as separate GitHub repositories, which the main repository conveniently presents as submodules. Accessible from the official Docker hub at the images have both Rosetta and PyRosetta pre-installed, such that Rosetta tutorials can be …
github.comThe Rosetta Bio-macromolecule modeling package. Available ...
## Repository files navigation # Rosetta Biomolecular Modeling Library The Rosetta software suite includes algorithms for computational modeling and analysis of protein structures. It has enabled notable scientific advances in computational biology, including de novo protein design, enzyme design, ligand docking, and structure prediction of biological macromolecules and macromolecular complexes. Rosetta is maintained by the RosettaCommons, a collaboration of 100+ academic research groups who have been developing Rosetta for over 20 years, and is available by license from the University of Washington. See for more information about Rosetta and the RosettaCommons. # Rosetta Code [...] Skip to content ## Navigation Menu Sign in Appearance settings Sign in Sign up Appearance settings You signed in with another tab or window. Reload …
github.comRosettaCommons/rosetta Rosetta · Discussions · GitHub
wendi-luo asked Feb 10, 2025 in Rosetta · Answered @wendi-luo @roccomoretti @wendi-luo @roccomoretti 5 Previous 1 2 3 Next You can’t perform that action at this time. [...] 5 You must be logged in to vote #️⃣ ### How to fix my spline file format? mkh-prot asked May 19, 2025 in Rosetta · Unanswered @mkh-prot @roccomoretti @mkh-prot @roccomoretti 1 You must be logged in to vote #️⃣ ### Cannot compute center of mass of zero residues! MuhammadAttaElhamouly asked Mar 21, 2025 in Rosetta · Unanswered @MuhammadAttaElhamouly @roccomoretti @PennGan @MuhammadAttaElhamouly @roccomoretti @PennGan 2 You must be logged in to vote #️⃣ ### NCAA Doug's Dock Design Minimize cheyenneluo asked Apr 24, 2025 in Rosetta · Unanswered @cheyenneluo @roccomoretti @cheyenneluo @roccomoretti 2 …
www.sciencedirect.comrosetta: a computer program for estimating soil hydraulic ...
by MG Schaap · 2001 · Cited by 3459 — We describe a computer program, rosetta, which implements five hierarchical pedotransfer functions (PTFs) for the estimation of water retention, and the
hub.docker.comrosettacommons/rosetta - Docker Image
Use for commercial purposes requires purchase of a separate license. (This includes fee-for-service work by academic users.) Please see or email [[email protected]](/cdn-cgi/l/email-protection#620e0b01070c11072217154c070617) for more information. #### Technical info [...] - System theme - Docker Suite Help System theme Docker Suite Sign inSign up ## rosettacommons/rosetta Byrosettacommons •Updated 8 months ago Official Rosetta/PyRosetta images maintained by RosettaCommons Image Machine learning & AI Data science 14 10K+ OverviewTags # rosettacommons/rosettarepository overview #### Official Rosetta/PyRosetta image maintained by rosettacommons.org Images are provided to academic and non-commercial users under the Rosetta Software Non-Commercial License Agreement and PyRosetta Software Non-Commercial License Agreement. These licenses applies to all images on this page. [...] ### Tag summary latest Content type Image Digest sha256:f5ea86a29… Size 2 GB …
rosettacommons.orgDownload - Rosetta Commons
### Actively Supported Software & Tools ExpandRosetta: A C++ software suite for modeling macromolecular structures. About Rosetta: Rosetta is software suite for modeling macromolecular structures. Its functionalities include structure prediction, design, and remodeling of proteins and nucleic acids. License Information: For non-commercial users (academic, non-profits, government) Rosetta is available for free under a license that can be found here. [...] Skip to content Rosetta Commons Rosetta Commons ### Software Download The Rosetta Commons community develops and supports a wide range of powerful tools for macromolecular modeling and design. These tools are freely available for non-commercial users, such as those who are a part of non-profits, academic institutions, and government agencies. For commercial users, our open-source tools are also available at …
esa.github.iopagmo 2.19.1 documentation
pagmo 2.19.1 documentation Repository Open issue .rst # Pagmo # Pagmo# _images/prob.png _images/algo.png _images/pop.png _images/island.png _images/archi.png _images/migration.png pagmo is a C++ scientific library for massively parallel optimization. It is built around the idea of providing a unified interface to optimization algorithms and problems, and to make their deployment in massively parallel environments easy. Efficient implementations of bio-inspired and evolutionary algorithms are sided to state-of-the-art optimization algorithms (Simplex Methods, SQP methods, interior points methods, …) and can be easily mixed (also with your newly-invented algorithms) to build a super-algorithm exploiting algorithmic cooperation via the asynchronous, generalized island model. [...] + 2.17.0 (2021-03-05) + 2.16.1 (2020-12-22) + 2.16.0 (2020-09-25) + 2.15.0 (2020-04-02) + 2.14.0 (2020-03-04) + 2.13.0 (2020-01-10) + 2.12.0 (2019-12-18) + …
github.comThe Rosetta Bio-macromolecule modeling package. Available ...
# Rosetta Code Organization Due to its size, Rosetta uses git submodules to help in organization. The main repository (RosettaCommons/rosetta) contains the Rosetta source code, database, unit test and integration tests rosetta/source/src -- The Rosetta source rosetta/database/ -- The Rosetta database (used during runtime) rosetta/source/test/ -- The compiled unit tests rosetta/tests/integration/ -- The integration tests rosetta/source/bin/ -- The location of the (symlinks to) the Rosetta executables -- (created during compilation) rosetta/source/build/ -- The location of the built libraries -- (created during compilation) Additional information is located in submodules: [...] ## Installing using Conda Rosetta binaries are avaliable as a `rosetta` Conda package in the RosettaCommons Conda Channel. All binaries are built using `serialization` and `cxx11thread` extras. Currently RosettaCommons has two …
forum.rosettacommons.orgWelcome to RosettaCommons | RosettaCommons
Jump to Navigation Home The hub for Rosetta modeling software A Team Approach Close collaboration between the labs the norm, even within single code modules. This allows for rapid enhancements and promotes the values of team science. Rosetta Software A dynamic and evolving macromolecular modeling suite addressing biomolecular structure prediction and design. Powered by the Commons RosettaCommons members enable notable scientific advanced in computational biology Rosetta's Breakthroughs + Design of a novel protein fold + Use of experimental data to solve or improve new macromolecular structures + High affinity redesign of protein-protein interfaces + Regular success in CASP and CAPRI challenges + Design of novel protein-protein interfaces Broad Functionality [...] It has enabled notable scientific advances in computational biology, including …
docs.rosettacommons.orgFAQ
#### What is RosettaCommons? RosettaCommons is a collection of 40+ groups and institutions from around the world which work together to develop and support Rosetta. See for more information. #### What is ROSIE? Robetta? There are a number of publicly accessible servers on the web that allow researchers to run certain Rosetta protocols without installing Rosetta locally. Robetta is the original Rosetta web server. ROSIE (the Rosetta Online Server that Includes Everyone) is a new, centralized site for Rosetta web servers, and includes a number of protocols. Other web servers also exist. #### What is PyRosetta? PyRosetta is a wrapper around the C++ Rosetta libraries, allowing them to be used from user-written Python scripts. See for more details. #### What …
researchmgt.monash.edu[PDF] GTOPX space mission benchmarks
2.6. Rosetta The Rosetta benchmark models multi gravity assist space mission to comet 67P/Churyumov-Gerasimenko, including deep space maneuvers (DSM). The sequence of fly-by planets for this mission is given by Earth–Earth–Mars–Earth–Earth-67P. The ob-jective of this benchmark is to minimize the total ∆V accumu-lated during the mission. The benchmark involves 22 decision variables (see Table 7): The best known solution to this benchmark has an objective function value of f (x) = 1.3434, and the vector of solution decision variables x is available online . 3 Martin Schlueter, Mehdi Neshat, Mohamed Wahib et al. SoftwareX 14 (2021) 100666 Fig. 1. Pareto front of Cassini1-MO. Fig. 2. Pareto front of Cassini1-MO-MINLP. [...] Rosetta Previous Best New solution (grid search) New solution (local …
www.midaco-solver.comGTOPX Space Benchmarks - MIDACO-SOLVER
GTOPX Benchmarks | | | | | | | | | --- --- --- --- | | No. | Benchmark Name | Ref | Objectives | Variables | Constraints | Solution f(x) | Landscape Analysis | | 1 | Cassini1 | NASA | 1 | 6 | 4 | 4.9307 | | 2 | Cassini2 | Wiki | 1 | 22 | 0 | 8.3830 | | 3 | Messenger (reduced) | NASA | 1 | 18 | 0 | 8.6299 | | 4 | Messenger (full) | Wiki | 1 | 26 | 0 | 1.9579 | | 5 | GTOC1 | ESA | 1 | 8 | 6 | -1581950.0 | | 6 | Rosetta | ESA | …
esa.intGlobal Trajectory Optimisation Problems Database
Problem details # Messenger (Full Mission) This trajectory optimisation problem represents a rendezvous mission to Mercury modelled as an MGA-1DSM problem. The selected fly-by sequence and other parameters are compatible with the Messenger mission. With respect to the problem Messenger (reduced) the fly-by sequence is more complex and allows for resonant fly-bys at Mercury to lower the arrival DV. Problem details # Cassini 2 [...] Problem details # MGA-1DSM Global Optimisation Problems The constraint on the spacecraft thrusting only only at planetary encounters is often unacceptable as it may results in trajectories that are not realistic or that use more propellant than necessary. The MGA-1DSM problem removes most of these limitations. It represents an interplanetary trajectory of a spacecraft equipped …
lopez-ibanez.euOptimization Challenges at the European Space Agency
31.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) import pykep import numpy as np vout = pk.fb_prop(v = [1,0,0],v_pla = [0,1,0], rp=2., beta=3.1415/2, mu_pla=1.) 3.47 µs ± 68.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) Optimization problems in Space ● MGA: Multiple Gravity Assist Interplanetary Trajectory ○ box-constrained, low dimension, possibly combinatorial, SO, MO ● MGA-1DSM: MGA where only one Deep Space Maneuver is allowed in each leg ○ box-constrained high dimension, possibly combinatorial, SO, MO ● MGA-LT: MGA Low-Thrust maneuvers ○ non linear constraint, high dimension, possibly combinatorial SO, MO ● Tours and multiple visits: ○ mainly combinatorial, similar to TSP variants, SO, MO Given a planetary …
www.researchgate.net(PDF) GTOPX space mission benchmarks
Rosetta benchmark models multi gravity assist space mission to comet 67P/Churyumov-Gerasimenko, including deep space maneuvers (DSM).
No recorded documents for this selection.
04Code & measured result
1 candidate attemptCode diff recorded excerpt+584−506
+ durations = np.array([+ 365.2423131,+ 707.7546444,+ 257.3238516,+ 730.4837236,+ 1850.0,+ ])+ launch = final_epoch - float(np.sum(durations))+ launch = float(np.clip(launch, start_lo, start_hi))+ nominal_dates = launch + np.concatenate(([0.0], np.cumsum(durations)[:-1]))+ fractions = np.array([+ 0.469187104,+ 0.810371727,+ 0.2758878,+ 0.119192979,+ 0.43674223,+ ])
