Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Rigid Body Equilibrium Problems - I00:49

Rigid Body Equilibrium Problems - I

4.2K
A rigid body is said to be in static equilibrium when the net force and the net torque acting on the system is equal to zero. To solve for rigid body equilibrium problems, do the following steps.
4.2K
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

1.1K
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
1.1K
Method of Joints: Problem Solving I01:30

Method of Joints: Problem Solving I

1.9K
The method of joints is a commonly used technique to analyze the forces in structural trusses. The method is based on the principle of equilibrium, which assumes that the truss members are connected by frictionless pins. The forces at each joint can be determined by considering the equilibrium of the forces acting on that joint. Consider a truss structure with two forces of 20 N and 10 N acting at joints C and D, respectively. The method of joints can be used to determine the forces FCB, FDC,...
1.9K
Method of Joints: Problem Solving II01:30

Method of Joints: Problem Solving II

1.7K
Consider a truss structure with frictionless joints fixed to a wall and roller support. If a force of 150 N is applied to joint A, the forces in each member of the truss can be determined using the method of joints.
1.7K
Stability of Equilibrium Configuration: Problem Solving01:13

Stability of Equilibrium Configuration: Problem Solving

1.2K
The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
Problem-solving in the context of the stability of equilibrium configuration...
1.2K
Optimization Problems01:26

Optimization Problems

220
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
220

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Inference in spreading processes with neural-network priors.

Physical review. E·2026
Same author

Dynamical cavity method for hypergraphs and its application to quenches in the k-XOR-SAT problem.

Physical review. E·2025
Same author

Integer traffic assignment problem: Algorithms and insights on random graphs.

Physical review. E·2025
Same author

Dynamical regimes of diffusion models.

Nature communications·2024
Same author

Sampling with flows, diffusion, and autoregressive neural networks from a spin-glass perspective.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Dynamical phase transitions in graph cellular automata.

Physical review. E·2024
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: May 2, 2026

Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 16, 2013

8.2K

Locked constraint satisfaction problems.

Lenka Zdeborová1, Marc Mézard

  • 1Université Paris-Sud, LPTMS, UMR8626, Bât. 100, Université Paris-Sud 91405 Orsay Cedex, France.

Physical Review Letters
|September 4, 2008
PubMed
Summary
This summary is machine-generated.

We introduce random "locked" constraint satisfaction problems. These problems become extremely hard to solve algorithmically in their "clustered" phase, posing challenges for optimization.

More Related Videos

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.2K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

2.5K

Related Experiment Videos

Last Updated: May 2, 2026

Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 16, 2013

8.2K
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.2K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

2.5K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Complexity

Background:

  • Constraint satisfaction problems (CSPs) are fundamental in artificial intelligence and computer science.
  • Understanding the phase transitions and computational hardness of random CSPs is crucial for algorithm development.

Purpose of the Study:

  • Introduce and analyze the random "locked" constraint satisfaction problem.
  • Investigate the "clustered" phase and its implications for algorithmic solvability.
  • Propose new benchmarks for computationally hard optimization problems.

Main Methods:

  • Theoretical analysis of random "locked" CSPs.
  • Examination of solution space structure under varying constraint densities.
  • Algorithmic performance evaluation on proposed benchmarks.

Main Results:

  • Identification of a broad "clustered" phase in random "locked" CSPs.
  • Demonstration that solution spaces in the clustered phase consist of many isolated points.
  • Confirmation that existing algorithms fail to find solutions in this hard phase.

Conclusions:

  • The "clustered" phase of random "locked" CSPs presents significant algorithmic challenges.
  • These problems serve as valuable benchmarks for testing the limits of optimization algorithms.
  • The study provides insights into the origins of typical hardness in these problems.