Read the equation
This relation defines f4: Optimization: x in the source section “g16_core · f4: Optimization: x”. Chooses an argument maximizing the displayed objective over the stated feasible set. Its interpretation depends on the source’s stated variables and conditions; this record does not assign an unprovided physical or cognitive meaning to them.
Terms & definitions
The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.
How it works
Read f4: Optimization: x* as the quantity being defined or updated by this relation.
Chooses an argument maximizing the displayed objective over the stated feasible set.
Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.
Assumptions & limits
This source expression was recovered from the pinned code revision. Its presence does not establish that the associated program was built, run, or scientifically validated.
Original context
Read the surrounding source passage
f.push_back({"DiscreteDynamics(x_{t+1}=F(x_t,u))-f2", [](double x, const Inputs& in){
return 0.9 * x + 0.1 * in.utility;
}});
// f3: Reaction-Diffusion: 6δ/δt = D∇^2x + f(x)
f.push_back({"ReactionDiffusion(D∇^2x+f(x))-f3", [](double x, const Inputs& in){
return x + 0.05 * (softsign(in.dx_norm) - x);
}});
// f4: Optimization: x* = arg max U(x)
f.push_back({"Optimization(argmax U(x))-f4", [](double x, const Inputs& in){
return x + 0.2 * softsign(in.utility - x);
}});
// f5: Bayesian Inference: P(θ|D) ∝ P(D|θ)P(θ)
f.push_back({"BayesianInference(P(θ|D)∝...)-f5", [](double x, const Inputs& in){
double post = (x + in.utility) / (1.0 + std::abs(in.utility));
return clamp01(post);
}});
// f6: Learning / Weight Update: W_{t+1} = W_t + ΔW
f.push_back({"LearningWeightUpdate(ΔW)-f6", [](double x, const Inputs& in){Exact original notation
f4: Optimization: x* = arg max U(x)