Abstract

We recall, "a priori," numeric energy expression: Energy Numbers $\begin{gathered}\mathcal{V}=\left\{f \mid \exists\left\{e_1, e_2, \ldots, e_n\right\} \in E \cup R\right\} \\ \mathcal{V}=\left\{f \mid \exists\left\{e_1, e_2, \ldots, e_n\right\} \in E, \text { and }: E \mapsto r \in R\right\} \\ \mathcal{V}=\left\{E \mid \exists\left\{a_1, \ldots, a_n\right\} \in E, E \not \neg r \in R\right\}\end{gathered}$ We now introduce the set of optimized energy numbers: ($H_a \in \mathcal{H}$ or $P^n = NP$ or $(P,\mathcal{L},F) = NP$). Based on our formulation of the bi-objective optimization task, we can make the following mathematical inferences: 1. If the optimized energy numbers set $\mathcal{N}_H$ is equal to the original energy numbers set $\mathcal{E}$, i.e. $\mathcal{N}_H = \mathcal{E}$, then the maximum optimization score is achieved, i.e. the bi-objective optimization task is solved. This implies that there exists at least one solution to the optimization problem and $H_a \in \mathcal{H}$, where $H_a$ is the hypothesis that states the existence of an efficient algorithm to solve the problem. 2. If the optimized energy numbers set $\mathcal{N}_H$ is a subset of the original energy numbers set $\mathcal{E}$, i.e. $\mathcal{N}_H \subset \mathcal{E}$, then the optimization score is less than the maximum score. This indicates that there may exist more efficient algorithms to solve the problem, and the hypothesis $H_a$ is still possible. 3. If the optimized energy numbers set $\mathcal{N}_H$ is a superset of the original energy numbers set $\mathcal{E}$, i.e. $\mathcal{N}_H \supset \mathcal{E}$, then the optimization score is higher than the maximum score. This implies that the optimization problem may be easier than initially thought, and $P^n = NP$, or at least some form of $NP$-completeness. 4. If the optimized energy numbers set $\mathcal{N}_H$ is a strict subset of the original energy numbers set $\mathcal{E}$, i.e. $\mathcal{N}_H \subsetneq \mathcal{E}$, and $P^n \neq NP$, then it can be concluded that the optimization problem is complex but there may exist algorithms that can efficiently approximate the solution. 5. If the optimized energy numbers set $\mathcal{N}_H$ is empty, i.e. $\mathcal{N}_H = \emptyset$, then it can be inferred that the optimization problem is infeasible, i.e. no efficient algorithm exists to solve it, and $H_a$ is false. 6. Comparing the two objectives in the bi-objective optimization task, we can make the following statements: - The first objective, $\frac{\delta_{v(f)}(\mathbf{v}, \mathbf{w_{max}})} {\langle \mathbf{v_f}, \mathbf{1_f}\rangle}$, measures the efficiency of the algorithm and its ability to find low energy numbers. - The second objective, $\rho(\mathcal{N}_H)$, measures the accuracy of the algorithm in terms of loss and perplexity on the HyperLanguageModel. - Therefore, by optimizing both objectives simultaneously, we aim to find an efficient algorithm that also minimizes the loss and perplexities on the HyperLanguageModel. - If the optimization task is successfully solved, then the algorithm achieves both high efficiency and high accuracy. This would imply that the algorithm is able to find low energy numbers effectively and also generalize well on the HyperLanguageModel. The optimized energy numbers aim to find a set of numbers $\mathbf{v}$ that maximize the bi-objective optimization task, while also minimizing the loss and perplexity of the HyperLanguageModel. This is achieved by finding the set of numbers that have the highest delta value and the lowest perplexity, resulting in a more optimized and efficient set of energy numbers. By comparing the optimized energy numbers to the original set, we can see that the optimized set may have a higher delta value and a lower perplexity, indicating that it is a better set of numbers for the given task. This shows that the optimized energy numbers have successfully achieved their goal of maximizing efficiency while minimizing loss and perplexity.