Optimization by Simulated Annealing¶
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680.
Cited by¶
7 citations across 7 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Annealing
- Combinatorial optimisation: simulated annealing accepts uphill moves freely at high "temperature," then cools its acceptance threshold so the search settles near a global optimum instead of the nearest local one — the Metropolis criterion is a direct transcription of the metallurgical protocol.
This sourceTransfers the metallurgical heat-then-cool protocol to combinatorial optimization, with the Metropolis acceptance rule as the direct transcription of the statistical-mechanical process.
- Combinatorial optimisation: simulated annealing accepts uphill moves freely at high "temperature," then cools its acceptance threshold so the search settles near a global optimum instead of the nearest local one — the Metropolis criterion is a direct transcription of the metallurgical protocol.
- Eventual Realisation of Possibility
- Information and search: any codeword in a finite alphabet used long enough is eventually sent; branch-and-bound, simulated annealing, and evolutionary algorithms reach every connected region given enough exploration.
This sourceIntroduces simulated annealing as stochastic search; the method converges to a global optimum with probability one given sufficient exploration — any connected region is eventually reached.
- Information and search: any codeword in a finite alphabet used long enough is eventually sent; branch-and-bound, simulated annealing, and evolutionary algorithms reach every connected region given enough exploration.
- Local Optimum
- The same transfer runs from engineering to personal decision-making, where simulated annealing's cooling schedule — high temperature early, low temperature late — ports as career advice (explore broadly early, exploit later) with the identical structural justification: early non-local search covers more basins before the cost of moving rises.
This sourceThe cooling schedule (high temperature early, low late) that escapes local optima by temporarily accepting worse states.
- The same transfer runs from engineering to personal decision-making, where simulated annealing's cooling schedule — high temperature early, low temperature late — ports as career advice (explore broadly early, exploit later) with the identical structural justification: early non-local search covers more basins before the cost of moving rises.
- Metastability
- The optimization community's intuition that a learner is "trapped in a local minimum," together with its engineering responses — simulated annealing, momentum, restart schedules — is the materials-science barrier-crossing picture ported into search.
This sourcePorts the materials-science barrier-crossing / annealing picture into combinatorial search to escape local minima.
- The optimization community's intuition that a learner is "trapped in a local minimum," together with its engineering responses — simulated annealing, momentum, restart schedules — is the materials-science barrier-crossing picture ported into search.
- Optimization Landscape
- Simulated annealing — the statistical-physics insight that slow cooling escapes local minima — transferred into combinatorial optimisation and then into stochastic-gradient methods in deep learning, the structural intuition being the same: increase exploration when topology is rugged.
This sourceImports the statistical-physics slow-cooling insight into combinatorial optimization to escape local minima.
- Simulated annealing — the statistical-physics insight that slow cooling escapes local minima — transferred into combinatorial optimisation and then into stochastic-gradient methods in deep learning, the structural intuition being the same: increase exploration when topology is rugged.
- Simulated Annealing
- Kirkpatrick, Gelatt, and Vecchi (1983)
This sourceFoundational paper introducing simulated annealing as a general-purpose optimization metaheuristic; recasts the Metropolis algorithm with temperature-controlled probabilistic acceptance, demonstrated on TSP and VLSI placement.
- Kirkpatrick, Gelatt, and Vecchi (1983)
Mechanisms¶
- Annealing or Perturbation Schedule
- Its honest failure mode is the cooling schedule itself: cool too fast and you quench into a mediocre local optimum; cool too slow and you burn budget wandering long after the good basin was in reach.
This sourceExplains that annealing quality depends on the cooling schedule and that rapid quenching can trap a search in a nonoptimal state.
- Its honest failure mode is the cooling schedule itself: cool too fast and you quench into a mediocre local optimum; cool too slow and you burn budget wandering long after the good basin was in reach.
Verification¶
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